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• A Complete Guide to Metrics in Google Analytics

  11 janvier 2024, par Erin

  There’s no denying that Google Analytics is the most popular web analytics solution today. Many marketers choose it to understand user behaviour. But when it offers so many different types of metrics, it can be overwhelming to choose which ones to focus on. In this article, we’ll dive into how metrics work in Google Analytics 4 and how to decide which metrics may be most useful to you, depending on your analytics needs.

  However, there are alternative web analytics solutions that can provide more accurate data and supplement GA’s existing features. Keep reading to learn how to overcome Google Analytics limitations so you can get the more out of your web analytics.

  What is a metric in Google Analytics ?

  In Google Analytics, a metric is a quantitative measurement or numerical data that provides insights into specific aspects of user behaviour. Metrics represent the counts or sums of user interactions, events or other data points. You can use GA metrics to better understand how people engage with a website or mobile app. 

  Unlike the previous Universal Analytics (the previous version of GA), GA4 is event-centric and has automated and simplified the event tracking process. Compared to Universal Analytics, GA4 is more user-centric and lets you hone in on individual user journeys. Some examples of common key metrics in GA4 are : 

  • Sessions : A group of user interactions on your website that occur within a specific time period. A session concludes when there is no user activity for 30 minutes.
  • Total Users : The cumulative count of individuals who accessed your site within a specified date range.
  • Engagement Rate : The percentage of visits to your website or app that included engagement (e.g., one more pageview, one or more conversion, etc.), determined by dividing engaged sessions by sessions.

  

  Metrics are invaluable when it comes to website and conversion optimisation. Whether you’re on the marketing team, creating content or designing web pages, understanding how your users interact with your digital platforms is essential.

  GA4 metrics vs. dimensions

  GA4 uses metrics to discuss quantitative measurements and dimensions as qualitative descriptors that provide additional context to metrics. To make things crystal clear, here are some examples of how metrics and dimensions are used together : 

  • “Session duration” = metric, “device type” = dimension 
    • In this situation, the dimension can segment the data by device type so you can optimise the user experience for different devices.
  • “Bounce rate” = metric, “traffic source/medium” = dimension 
    • Here, the dimension helps you segment by traffic source to understand how different acquisition channels are performing. 
  • “Conversion rate” = metric, “Landing page” = dimension 
    • When the conversion rate data is segmented by landing page, you can better see the most effective landing pages. 

  You can get into the nitty gritty of granular analysis by combining metrics and dimensions to better understand specific user interactions.

  How do Google Analytics metrics work ?

  Before diving into the most important metrics you should track, let’s review how metrics in GA4 work. 

  

  1. Tracking code implementation

  The process begins with implementing Google Analytics 4 tracking code into the HTML of web pages. This tracking code is JavaScript added to each website page — it collects data related to user interactions, events and other important tidbits.

  2. Data collection

  As users interact with the website or app, the Google Analytics 4 tracking code captures various data points (i.e., page views, clicks, form submissions, custom events, etc.). This raw data is compiled and sent to Google Analytics servers for processing.

  3. Data processing algorithms

  When the data reaches Google Analytics servers, data processing algorithms come into play. These algorithms analyse the incoming raw data to identify the dataset’s trends, relationships and patterns. This part of the process involves cleaning and organising the data.

  4. Segmentation and customisation

  As discussed in the previous section, Google Analytics 4 allows for segmentation and customisation of data with dimensions. To analyse specific data groups, you can define segments based on various dimensions (e.g., traffic source, device type). Custom events and user properties can also be defined to tailor the tracking to the unique needs of your website or app.

  5. Report generation

  Google Analytics 4 can make comprehensive reports and dashboards based on the processed and segmented data. These reports, often in the form of graphs and charts, help identify patterns and trends in the data.

  What are the most important Google Analytics metrics to track ? 

  In this section, we’ll identify and define key metrics for marketing teams to track in Google Analytics 4. 

  

  1. Pageviews are the total number of times a specific page or screen on your website or app is viewed by visitors. Pageviews are calculated each time a web page is loaded or reloaded in a browser. You can use this metric to measure the popularity of certain content on your website and what users are interested in. 
  2. Event tracking monitors user interactions with content on a website or app (i.e., clicks, downloads, video views, etc.). Event tracking provides detailed insights into user engagement so you can better understand how users interact with dynamic content. 
  3. Retention rate can be analysed with a pre-made overview report that Google Analytics 4 provides. This user metric measures the percentage of visitors who return to your website or app after their first visit within a specific time period. Retention rate = (users with subsequent visits / total users in the initial cohort) x 100. Use this information to understand how relevant or effective your content, user experience and marketing efforts are in retaining visitors. You probably have more loyal/returning buyers if you have a high retention rate. 
  4. Average session duration calculates the average time users spend on your website or app per session. Average session duration = total duration of all sessions / # of sessions. A high average session duration indicates how interested and engaged users are with your content. 
  5. Site searches and search queries on your website are automatically tracked by Google Analytics 4. These metrics include search terms, number of searches and user engagement post-search. You can use site search metrics to better understand user intent and refine content based on users’ searches. 
  6. Entrance and exit pages show where users first enter and leave your site. This metric is calculated by the percentage of sessions that start or end on a specific page. Knowing where users are entering and leaving your site can help identify places for content optimisation. 
  7. Device and browser info includes data about which devices and browsers websites or apps visitors use. This is another metric that Google Analytics 4 automatically collects and categorises during user sessions. You can use this data to improve the user experience on relevant devices and browsers. 
  8. Bounce rate is the percentage of single-page sessions where users leave your site or app without interacting further. Bounce rate = (# of single-page sessions / total # of sessions) x 100. Bounce rate is useful for determining how effective your landing pages are — pages with high bounce rates can be tweaked and optimised to enhance user engagement.

  Examples of how Matomo can elevate your web analytics

  Although Google Analytics is a powerful tool for understanding user behaviour, it also has privacy concerns, limitations and a list of issues. Another web analytics solution like Matomo can help fill those gaps so you can get the most out of your analytics.

  

  1. Cross-verify and validate your observations from Google Analytics by comparing data from Matomo’s Heatmaps and Session Recordings for the same pages. This process grants you access to these advanced features that GA4 does not offer.

  

  1. Matomo provides you with greater accuracy thanks to its privacy-friendly design. Unlike GA4, Matomo can be configured to operate without cookies. This means increased accuracy without intrusive cookie consent screens interrupting the user experience. It’s a win for you and for your users. Matomo also doesn’t apply data sampling so you can rest assured that the data you see is 100% accurate.

  3. Unlike GA4, Matomo offers direct access to customer support so you can save time sifting through community forum threads and online documentation. Gain personalised assistance and guidance for your analytics questions, and resolve issues efficiently.

  

  4. Matomo’s Form Analytics and Media Analytics extend your analytics capabilities beyond just pageviews and event tracking.

     Tracking user interactions with forms can tell you which fields users struggle with, common drop-off points, in addition to which parts of the form successfully guide visitors towards submission.

     See first-hand how Concrete CMS 3x their leads using Matomo’s Form Analytics.

     Media Analytics can provide insight into how users interact with image, video, or audio content on your website. You can use this feature to assess the relevance and popularity of specific content by knowing what your audience is engaged by.

  Try Matomo for Free

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  Final thoughts

  Although Google Analytics is a powerful tool on its own, Matomo can elevate your web analytics by offering advanced features, data accuracy and a privacy-friendly design. Don’t play a guessing game with your data — Matomo provides 100% accurate data so you don’t have to rely on AI or machine learning to fill in the gaps. Matomo can be configured cookieless which also provides you with more accurate data and a better user experience. 

  Lastly, Matomo is fully compliant with some of the world’s strictest privacy regulations like GPDR. You won’t have to sacrifice compliance for accurate, high quality data. 

  Start your 21-day free trial of Matomo — no credit card required.

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• The first in-depth technical analysis of VP8

    
  19 mai 2010, par Dark Shikari — VP8, google

  Back in my original post about Internet video, I made some initial comments on the hope that VP8 would solve the problems of web video by providing a supposed patent-free video format with significantly better compression than the current options of Theora and Dirac. Fortunately, it seems I was able to acquire access to the VP8 spec, software, and source a good few days before the official release and so was able to perform a detailed technical analysis in time for the official release.

  The questions I will try to answer here are :

  1. How good is VP8 ? Is the file format actually better than H.264 in terms of compression, and could a good VP8 encoder beat x264 ? On2 claimed 50% better than H.264, but On2 has always made absurd claims that they were never able to back up with results, so such a number is almost surely wrong. VP7, for example, was claimed to be 15% better than H.264 while being much faster, but was in reality neither faster nor higher quality.

  2. How good is On2′s VP8 implementation ? Irrespective of how good the spec is, is the implementation good, or is this going to be just like VP3, where On2 releases an unusably bad implementation with the hope that the community will fix it for them ? Let’s hope not ; it took 6 years to fix Theora !

  3. How likely is VP8 to actually be free of patents ? Even if VP8 is worse than H.264, being patent-free is still a useful attribute for obvious reasons. But as noted in my previous post, merely being published by Google doesn’t guarantee that it is. Microsoft did similar a few years ago with the release of VC-1, which was claimed to be patent-free — but within mere months after release, a whole bunch of companies claimed patents on it and soon enough a patent pool was formed.

  We’ll start by going through the core features of VP8. We’ll primarily analyze them by comparing to existing video formats. Keep in mind that an encoder and a spec are two different things : it’s possible for good encoder to be written for a bad spec or vice versa ! Hence why a really good MPEG-1 encoder can beat a horrific H.264 encoder.

  But first, a comment on the spec itself.

  AAAAAAAGGGGGGGGGGGGGHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH !

  The spec consists largely of C code copy-pasted from the VP8 source code — up to and including TODOs, “optimizations”, and even C-specific hacks, such as workarounds for the undefined behavior of signed right shift on negative numbers. In many places it is simply outright opaque. Copy-pasted C code is not a spec. I may have complained about the H.264 spec being overly verbose, but at least it’s precise. The VP8 spec, by comparison, is imprecise, unclear, and overly short, leaving many portions of the format very vaguely explained. Some parts even explicitly refuse to fully explain a particular feature, pointing to highly-optimized, nigh-impossible-to-understand reference code for an explanation. There’s no way in hell anyone could write a decoder solely with this spec alone.

  Now that I’ve gotten that out of my system, let’s get back to VP8 itself. To begin with, to get a general sense for where all this fits in, basically all modern video formats work via some variation on the following chain of steps :

  Encode : Predict -> Transform + Quant -> Entropy Code -> Loopfilter
  Decode : Entropy Decode -> Predict -> Dequant + Inverse Transform -> Loopfilter

  If you’re looking to just get to the results and skip the gritty technical details, make sure to check out the “overall verdict” section and the “visual results” section. Or at least skip to the “summary for the lazy”.
  

  Prediction

  Prediction is any step which attempts to guess the content of an area of the frame. This could include functions based on already-known pixels in the same frame (e.g. inpainting) or motion compensation from a previous frame. Prediction usually involves side data, such as a signal telling the decoder a motion vector to use for said motion compensation.

  Intra Prediction

  Intra prediction is used to guess the content of a block without referring to other frames. VP8′s intra prediction is basically ripped off wholesale from H.264 : the “subblock” prediction modes are almost exactly identical (they even have the same names !) to H.264′s i4x4 mode, and the whole block prediction mode is basically identical to i16x16. Chroma prediction modes are practically identical as well. i8x8, from H.264 High Profile, is not present. An additional difference is that the planar prediction mode has been replaced with TM_PRED, a very vaguely similar analogue. The specific prediction modes are internally slightly different, but have the same names as in H.264.

  Honestly, I’m very disappointed here. While H.264′s intra prediction is good, it has certainly been improved on quite a bit over the past 7 years, and I thought that blatantly ripping it off was the domain of companies like Real (see RV40). I expected at least something slightly more creative out of On2. But more important than any of that : this is a patent time-bomb waiting to happen. H.264′s spatial intra prediction is covered in patents and I don’t think that On2 will be able to just get away with changing the rounding in the prediction modes. I’d like to see Google’s justification for this — they must have a good explanation for why they think there won’t be any patent issues.

  Update : spatial intra prediction apparently dates back to Nokia’s MVC H.26L proposal, from around 2000. It’s possible that Google believes that this is sufficient prior art to invalidate existing patents — which is not at all unreasonable !

  Verdict on Intra Prediction : Slightly modified ripoff of H.264. Somewhat worse than H.264 due to omission of i8x8.

  Inter Prediction

  Inter prediction is used to guess the content of a block by referring to past frames. There are two primary components to inter prediction : reference frames and motion vectors. The reference frame is a past frame from which to grab pixels from and the motion vectors index an offset into that frame. VP8 supports a total of 3 reference frames : the previous frame, the “alt ref” frame, and the “golden frame”. For motion vectors, VP8 supports variable-size partitions much like H.264. For subpixel precision, it supports quarter-pel motion vectors with a 6-tap interpolation filter. In short :

  VP8 reference frames : up to 3
  H.264 reference frames : up to 16
  VP8 partition types : 16×16, 16×8, 8×16, 8×8, 4×4
  H.264 partition types : 16×16, 16×8, 8×16, flexible subpartitions (each 8×8 can be 8×8, 8×4, 4×8, or 4×4).
  VP8 chroma MV derivation : each 4×4 chroma block uses the average of colocated luma MVs (same as MPEG-4 ASP)
  H.264 chroma MV derivation : chroma uses luma MVs directly
  VP8 interpolation filter : qpel, 6-tap luma, mixed 4/6-tap chroma
  H.264 interpolation filter : qpel, 6-tap luma (staged filter), bilinear chroma
  H.264 has but VP8 doesn’t : B-frames, weighted prediction

  H.264 has a significantly better and more flexible referencing structure. Sub-8×8 partitions are mostly unnecessary, so VP8′s omission of the H.264-style subpartitions has little consequence. The chroma MV derivation is more accurate in H.264 but slightly slower ; in practice the difference is probably near-zero both speed and compression-wise, since sub-8×8 luma partitions are rarely used (and I would suspect the same carries over to VP8).

  The VP8 interpolation filter is likely slightly better, but will definitely be slower to implement, both encoder and decoder-side. A staged filter allows the encoder to precalculate all possible halfpel positions and then quickly calculate qpel positions when necessary : an unstaged filter does not, making subpel motion estimation much slower. Not that unstaged filters are bad — staged filters have basically been abandoned for all of the H.265 proposals — it’s just an inherent disadvantage performance-wise. Additionally, having as high as 6 taps on chroma is, IMO, completely unnecessary and wasteful.

  The lack of B-frames in VP8 is a killer. B-frames can give 10-20% (or more) compression benefit for minimal speed cost ; their omission in VP8 probably costs more compression than all other problems noted in this post combined. This was not unexpected, however ; On2 has never used B-frames in any of their video formats. They also likely present serious patent problems, which probably explains their omission. Lack of weighted prediction is also going to hurt a bit, especially in fades.

  Update : Alt-ref frames can apparently be used to partially replicate the lack of B-frames. It’s not nearly as good, but it can get at least some of the benefit without actual B-frames.

  Verdict on Inter Prediction : Similar partitioning structure to H.264. Much weaker referencing structure. More complex, slightly better interpolation filter. Mostly a wash — except for the lack of B-frames, which is seriously going to hurt compression.
  

  Transform and Quantization
  

  After prediction, the encoder takes the difference between the prediction and the actual source pixels (the residual), transforms it, and quantizes it. The transform step is designed to make the data more amenable to compression by decorrelating it. The quantization step is the actual information-losing step where compression occurs ; the output values of transform are rounded, mostly to zero, leaving only a few integer coefficients.

  Transform

  For transform, VP8 uses again a very H.264-reminiscent scheme. Each 16×16 macroblock is divided into 16 4×4 DCT blocks, each of which is transformed by a bit-exact DCT approximation. Then, the DC coefficients of each block are collected into another 4×4 group, which is then Hadamard-transformed. OK, so this isn’t reminiscent of H.264, this is H.264. There are, however, 3 differences between VP8′s scheme and H.264′s.

  The first is that the 8×8 transform is omitted entirely (fitting with the omission of the i8x8 intra mode). The second is the specifics of the transform itself. H.264 uses an extremely simplified “DCT” which is so un-DCT-like that it often referred to as the HCT (H.264 Cosine Transform) instead. This simplified transform results in roughly 1% worse compression, but greatly simplifies the transform itself, which can be implemented entirely with adds, subtracts, and right shifts by 1. VC-1 uses a more accurate version that relies on a few small multiplies (numbers like 17, 22, 10, etc). VP8 uses an extremely, needlessly accurate version that uses very large multiplies (20091 and 35468). This in retrospect is not surpising, as it is very similar to what VP3 used.

  The third difference is that the Hadamard hierarchical transform is applied for some inter blocks, not merely i16x16. In particular, it also runs for p16x16 blocks. While this is definitely a good idea, especially given the small transform size (and the need to decorrelate the DC value between the small transforms), I’m not quite sure I agree with the decision to limit it to p16x16 blocks ; it seems that perhaps with a small amount of modification this could also be useful for other motion partitions. Also, note that unlike H.264, the hierarchical transform is luma-only and not applied to chroma.

  Overall, the transform scheme in VP8 is definitely weaker than in H.264. The lack of an 8×8 transform is going to have a significant impact on detail retention, especially at high resolutions. The transform is needlessly slower than necessary as well, though a shift-based transform might be out of the question due to patents. The one good new idea here is applying the hierarchical DC transform to inter blocks.

  Verdict on Transform : Similar to H.264. Slower, slightly more accurate 4×4 transform. Improved DC transform for luma (but not on chroma). No 8×8 transform. Overall, worse.

  Quantization

  For quantization, the core process is basically the same among all MPEG-like video formats, and VP8 is no exception. The primary ways that video formats tend to differentiate themselves here is by varying quantization scaling factors. There are two ways in which this is primarily done : frame-based offsets that apply to all coefficients or just some portion of them, and macroblock-level offsets. VP8 primarily uses the former ; in a scheme much less flexible than H.264′s custom quantization matrices, it allows for adjusting the quantizer of luma DC, luma AC, chroma DC, and so forth, separately. The latter (macroblock-level quantizer choice) can, in theory, be done using its “segmentation map” features, albeit very hackily and not very efficiently.

  The killer mistake that VP8 has made here is not making macroblock-level quantization a core feature of VP8. Algorithms that take advantage of macroblock-level quantization are known as “adaptive quantization” and are absolutely critical to competitive visual quality. My implementation of variance-based adaptive quantization (before, after) in x264 still stands to this day as the single largest visual quality gain in x264 history. Encoder comparisons have showed over and over that encoders without adaptive quantization simply cannot compete.

  Thus, while adaptive quantization is possible in VP8, the only way to implement it is to define one segment map for every single quantizer that one wants and to code the segment map index for every macroblock. This is inefficient and cumbersome ; even the relatively suboptimal MPEG-style delta quantizer system would be a better option. Furthermore, only 4 segment maps are allowed, for a maximum of 4 quantizers per frame.

  Verdict on Quantization : Lack of well-integrated adaptive quantization is going to be a killer when the time comes to implement psy optimizations. Overall, much worse.

  Entropy Coding

  Entropy coding is the process of taking all the information from all the other processes : DCT coefficients, prediction modes, motion vectors, and so forth — and compressing them losslessly into the final output file. VP8 uses an arithmetic coder somewhat similar to H.264′s, but with a few critical differences. First, it omits the range/probability table in favor of a multiplication. Second, it is entirely non-adaptive : unlike H.264′s, which adapts after every bit decoded, probability values are constant over the course of the frame. Accordingly, the encoder may periodically send updated probability values in frame headers for some syntax elements. Keyframes reset the probability values to the defaults.

  This approach isn’t surprising ; VP5 and VP6 (and probably VP7) also used non-adaptive arithmetic coders. How much of a penalty this actually means compression-wise is unknown ; it’s not easy to measure given the design of either H.264 or VP8. More importantly, I question the reason for this : making it adaptive would add just one single table lookup to the arithmetic decoding function — hardly a very large performance impact.

  Of course, the arithmetic coder is not the only part of entropy coding : an arithmetic coder merely turns 0s and 1s into an output bitstream. The process of creating those 0s and 1s and selecting the probabilities for the encoder to use is an equally interesting problem. Since this is a very complicated part of the video format, I’ll just comment on the parts that I found particularly notable.

  Motion vector coding consists of two parts : prediction based on neighboring motion vectors and the actual compression of the resulting delta between that and the actual motion vector. The prediction scheme in VP8 is a bit odd — worse, the section of the spec covering this contains no English explanation, just confusingly-written C code. As far as I can tell, it chooses an arithmetic coding context based on the neighboring MVs, then decides which of the predicted motion vectors to use, or whether to code a delta instead.

  The downside of this scheme is that, like in VP3/Theora (though not nearly as badly), it biases heavily towards the re-use of previous motion vectors. This is dangerous because, as the Theora devs have recently found (and fixed to some extent in Theora 1.2 aka Ptalabvorm), any situation in which the encoder picks a motion vector which isn’t the “real” motion vector in order to save bits can potentially have negative visual consequences. In terms of raw efficiency, I’m not sure whether VP8 or H.264′s prediction is better here.

  The compression of the resulting delta is similar to H.264, except for the coding of very large deltas, which is slightly better (similar to FFV1′s Golomb-like arithmetic codes).

  Intra prediction mode coding is done using arithmetic coding contexts based on the modes of the neighboring blocks. This is probably a good bit better than the hackneyed method that H.264 uses, which always struck me as being poorly designed.

  Residual coding is even more difficult to understand than motion vector coding, as the only full reference is a bunch of highly optimized, highly obfuscated C code. Like H.264′s CAVLC, it bases contexts on the number of nonzero coefficients in the top and left blocks relative to the current block. In addition, it also considers the magnitude of those coefficients and, like H.264′s CABAC, updates as coefficients are decoded.

  One more thing to note is the data partitioning scheme used by VP8. This scheme is much like VP3/Theora’s and involves putting each syntax element in its own component of the bitstream. The unfortunate problem with this is that it’s a nightmare for hardware implementations, greatly increasing memory bandwidth requirements. I have already received a complaint from a hardware developer about this specific feature with regard to VP8.

  Verdict on Entropy Coding : I’m not quite sure here. It’s better in some ways, worse in some ways, and just plain weird in others. My hunch is that it’s probably a very slight win for H.264 ; non-adaptive arithmetic coding has to have some serious penalties. It may also be a hardware implementation problem.
  

  Loop Filter

  The loop filter is run after decoding or encoding a frame and serves to perform extra processing on a frame, usually to remove blockiness in DCT-based video formats. Unlike postprocessing, this is not only for visual reasons, but also to improve prediction for future frames. Thus, it has to be done identically in both the encoder and decoder. VP8′s loop filter is vaguely similar to H.264′s, but with a few differences. First, it has two modes (which can be chosen by the encoder) : a fast mode and a normal mode. The fast mode is somewhat simpler than H.264′s, while the normal mode is somewhat more complex. Secondly, when filtering between macroblocks, VP8′s filter has wider range than the in-macroblock filter — H.264 did this, but only for intra edges.

  Third, VP8′s filter omits most of the adaptive strength mechanics inherent in H.264′s filter. Its only adaptation is that it skips filtering on p16x16 blocks with no coefficients. This may be responsible for the high blurriness of VP8′s loop filter : it will run over and over and over again on all parts of a macroblock even if they are unchanged between frames (as long as some other part of the macroblock is changed). H.264′s, by comparison, is strength-adaptive based on whether DCT coefficients exist on either side of a given edge and based on the motion vector delta and reference frame delta across said edge. Of course, skipping this strength calculation saves some decoding time as well.

  Update :
  05:28 < derf> Gumboot : You’ll be disappointed to know they got the loop filter ordering wrong again.
  05:29 < derf> Dark_Shikari : They ordered it such that you have to process each macroblock in full before processing the next one.

  Verdict on Loop Filter : Definitely worse compression-wise than H.264′s due to the lack of adaptive strength. Especially with the “fast” mode, might be significantly faster. I worry about it being too blurry.

  Overall verdict on the VP8 video format

  Overall, VP8 appears to be significantly weaker than H.264 compression-wise. The primary weaknesses mentioned above are the lack of proper adaptive quantization, lack of B-frames, lack of an 8×8 transform, and non-adaptive loop filter. With this in mind, I expect VP8 to be more comparable to VC-1 or H.264 Baseline Profile than with H.264. Of course, this is still significantly better than Theora, and in my tests it beats Dirac quite handily as well.

  Supposedly Google is open to improving the bitstream format — but this seems to conflict with the fact that they got so many different companies to announce VP8 support. The more software that supports a file format, the harder it is to change said format, so I’m dubious of any claim that we will be able to spend the next 6-12 months revising VP8. In short, it seems to have been released too early : it would have been better off to have an initial period during which revisions could be submitted and then a big announcement later when it’s completed.

  Update : it seems that Google is not open to changing the spec : it is apparently “final”, complete with all its flaws.

  In terms of decoding speed I’m not quite sure ; the current implementation appears to be about 16% slower than ffmpeg’s H.264 decoder (and thus probably about 25-35% slower than state-of-the-art decoders like CoreAVC). Of course, this doesn’t necessarily say too much about what a fully optimized implementation will reach, but the current one seems to be reasonably well-optimized and has SIMD assembly code for almost all major DSP functions, so I doubt it will get that much faster.

  I would expect, with equally optimized implementations, VP8 and H.264 to be relatively comparable in terms of decoding speed. This, of course, is not really a plus for VP8 : H.264 has a great deal of hardware support, while VP8 largely has to rely on software decoders, so being “just as fast” is in many ways not good enough. By comparison, Theora decodes almost 35% faster than H.264 using ffmpeg’s decoder.

  Finally, the problem of patents appears to be rearing its ugly head again. VP8 is simply way too similar to H.264 : a pithy, if slightly inaccurate, description of VP8 would be “H.264 Baseline Profile with a better entropy coder”. Even VC-1 differed more from H.264 than VP8 does, and even VC-1 didn’t manage to escape the clutches of software patents. It’s quite possible that VP8 has no patent issues, but until we get some hard evidence that VP8 is safe, I would be cautious. Since Google is not indemnifying users of VP8 from patent lawsuits, this is even more of a potential problem. Most importantly, Google has not released any justifications for why the various parts of VP8 do not violate patents, as Sun did with their OMS standard : such information would certainly cut down on speculation and make it more clear what their position actually is.

  But if luck is on Google’s side and VP8 does pass through the patent gauntlet unscathed, it will undoubtedly be a major upgrade as compared to Theora.

  Addendum A : On2′s VP8 Encoder and Decoder

  This post is primarily aimed at discussing issues relating to the VP8 video format. But from a practical perspective, while software can be rewritten and improved, to someone looking to use VP8 in the near future, the quality (both code-wise, compression-wise, and speed-wise) of the official VP8 encoder and decoder is more important than anything I’ve said above. Thus, after reading through most of the code, here’s my thoughts on the software.

  Initially I was intending to go easy on On2 here ; I assumed that this encoder was in fact new for VP8 and thus they wouldn’t necessarily have time to make the code high-quality and improve its algorithms. However, as I read through the encoder, it became clear that this was not at all true ; there were comments describing bugfixes dating as far back as early 2004. That’s right : this software is even older than x264 ! I’m guessing that the current VP8 software simply evolved from the original VP7 software. Anyways, this means that I’m not going to go easy on On2 ; they’ve had (at least) 6 years to work on VP8, and a much larger dev team than x264′s to boot.

  Before I tear the encoder apart, keep in mind that it isn’t bad. In fact, compression-wise, I don’t think they’re going to be able to get it that much better using standard methods. I would guess that the encoder, on slowest settings, is within 5-10% of the maximum PSNR that they’ll ever get out of it. There’s definitely a whole lot more to be had using unusual algorithms like MB-tree, not to mention the complete lack of psy optimizations — but at what it tries to do, it does pretty decently. This is in contrast to the VP3 encoder, which was a pile of garbage (just ask any Theora dev).

  Before I go into specific components, a general note on code quality. The code quality is much better than VP3, though there’s still tons of typos in the comments. They also appear to be using comments as a form of version control system, which is a bit bizarre. The assembly code is much worse, with staggering levels of copy-paste coding, some completely useless instructions that do nothing at all, unaligned loads/stores to what-should-be aligned data structures, and a few functions that are simply written in unfathomably roundabout (and slower) ways. While the C code isn’t half bad, the assembly is clearly written by retarded monkeys. But I’m being unfair : this is way better than with VP3.

  Motion estimation : Diamond, hex, and exhaustive (full) searches available. All are pretty naively implemented : hexagon, for example, performs a staggering amount of redundant work (almost half of the locations it searches are repeated !). Full is even worse in terms of inefficiency, but it’s useless for all but placebo-level speeds, so I’m not really going to complain about that.

  Subpixel motion estimation : Straightforward iterative diamond and square searches. Nothing particularly interesting here.

  Quantization : Primary quantization has two modes : a fast mode and a slightly slower mode. The former is just straightforward deadzone quant, while the latter has a bias based on zero-run length (not quite sure how much this helps, but I like the idea). After this they have “coefficient optimization” with two modes. One mode simply tries moving each nonzero coefficient towards zero ; the slow mode tries all 2^16 possible DCT coefficient rounding permutations. Whoever wrote this needs to learn what trellis quantization (the dynamic programming solution to the problem) is and stop using exponential-time algorithms in encoders.

  Ratecontrol (frame type handling) : Relies on “boosting” the quality of golden frames and “alt-ref” frames — a concept I find extraordinarily dubious because it means that the video will periodically “jump” to a higher quality level, which looks utterly terrible in practice. You can see the effect in this graph of PSNR ; every dozen frames or so, the quality “jumps”. This cannot possibly look good in motion.

  Ratecontrol (overall) : Relies on a purely reactive ratecontrol algorithm, which probably will not do very well in difficult situations such as hard-CBR and tight buffer constraints. Furthermore, it does no adaptation of the quantizer within the frame (e.g. in the case that the frame overshot the size limitations ratecontrol put on it). Instead, it relies on re-encoding the frame repeatedly to reach the target size — which in practice is simply not a usable option for two reasons. In low-latency situations where one can’t have a large delay, re-encoding repeatedly may send the encoder way behind time-wise. In any other situation, one can afford to use frame-based threading, a much faster algorithm for multithreaded encoding than the typical slice-based threading — which makes re-encoding impossible.

  Loop filter : The encoder attempts to optimize the loop filter parameters for maximum PSNR. I’m not quite sure how good an idea this is ; every example I’ve seen of this with H.264 ends up creating very bad (often blurry) visual results.

  Overall performance : Even on the absolute fastest settings with multithreading, their encoder is slow. On my 1.6Ghz Core i7 it gets barely 26fps encoding 1080p ; not even enough to reliably do real-time compression. x264, by comparison, gets 101fps at its fastest preset “ultrafast”. Now, sure, I don’t expect On2′s encoder to be anywhere near as fast as x264, but being unable to stream HD video on a modern quad-core system is simply not reasonable in 2010. Additionally, the speed options are extraordinarily confusing and counterintuitive and don’t always seem to work properly ; for example, fast encoding mode (–rt) seems to be ignored completely in 2-pass.

  Overall compression : As said before, compression-wise the encoder does a pretty good job with the spec that it’s given. The slower algorithms in the encoder are clearly horrifically unoptimized (see the comments on motion search and quantization in particular), but they still work.

  Decoder : Seems to be straightforward enough. Nothing jumped out at me as particularly bad, slow, or otherwise, besides the code quality issues mentioned above.

  Practical problems : The encoder and decoder share a staggering amount of code. This means that any bug in the common code will affect both, and thus won’t be spotted because it will affect them both in a matching fashion.  This is the inherent problem with any file format that doesn’t have independent implementations and is defined by a piece of software instead of a spec : there are always bugs. RV40 had a hilarious example of this, where a typo of “22″ instead of “33″ resulted in quarter-pixel motion compensation being broken. Accordingly, I am very dubious of any file format defined by software instead of a specification. Google should wait until independent implementations have been created before setting the spec in stone.

  Update : it seems that what I forsaw is already coming true :

  <derf> gmaxwell : It survives it with a patch that causes artifacts because their encoder doesn’t clamp MVs properly.
  <gmaxwell> ::cries: :
  <derf> So they reverted my decoder patch, instead of fixing the encoder.
  <gmaxwell> “but we have many files encoded with this !”
  <gmaxwell> so great.. single implementation and it depends on its own bugs.

  This is just like Internet Explorer 6 all over again — bugs in the software become part of the “spec” !

  Hard PSNR numbers :
  (Source/target bitrate are the same as in my upcoming comparison.)
  x264, slowest mode, High Profile : 29.76103db ( 28% better than VP8)
  VP8, slowest mode : 28.37708db ( 8.5% better than x264 baseline)
  x264, slowest mode, Baseline Profile : 27.95594db

  Note that these numbers are a “best-case” situation : we’re testing all three optimized for PSNR, which is what the current VP8 encoder specializes in as well. This is not too different from my expectations above as estimated from the spec itself ; it’s relatively close to x264′s Baseline Profile.

  Keep in mind that this is not representative of what you can get out of VP8 now, but rather what could be gotten out of VP8. PSNR is meaningless for real-world encoding — what matters is visual quality — so hopefully if problems like the adaptive quantization issue mentioned previously can be overcome, the VP8 encoder could be improved to have x264-level psy optimizations. However, as things stand…

  Visual results : Unfortunately, since the current VP8 encoder optimizes entirely for PSNR, the visual results are less than impressive. Here’s a sampling of how it compares with some other encoders. Source and bitrate are the same as above ; all encoders are optimized for optimal visual quality wherever possible. And apparently given some of the responses to this part, many people cannot actually read ; the bitrate is (as close as possible to) the same on all of these files.

  Update : I got completely slashdotted and my few hundred gigs of bandwidth ran out in mere hours. The images below have been rehosted, so if you’ve pasted the link somewhere else, check below for the new one.

  VP8 (On2 VP8 rc8) (source) (Note : I recently realized that the official encoder doesn’t output MKV, so despite the name, this file is actually a VP8 bitstream wrapped in IVF, as generated by ivfenc. Decode it with ivfdec.)
  H.264 (Recent x264) (source)
  H.264 Baseline Profile (Recent x264) (source)
  Theora (Recent ptalabvorm nightly) (source)
  Dirac (Schroedinger 1.0.9) (source)
  VC-1 (Microsoft VC-1 SDK) (source)
  MPEG-4 ASP (Xvid 1.2.2) (source)

  The quality generated by On2′s VP8 encoder will probably not improve significantly without serious psy optimizations.

  One further note about the encoder : currently it will drop frames by default, which is incredibly aggravating and may cause serious problems. I strongly suggest anyone using it to turn the frame-dropping feature off in the options.

  Addendum B : Google’s choice of container and audio format for HTML5

  Google has chosen Matroska for their container format. This isn’t particularly surprising : Matroska is one of the most widely used “modern” container formats and is in many ways best-suited to the task. MP4 (aka ISOmedia) is probably a better-designed format, but is not very flexible ; while in theory it can stick anything in a private stream, a standardization process is technically necessary to “officially” support any new video or audio formats. Patents are probably a non-issue ; the MP4 patent pool was recently disbanded, largely because nobody used any of the features that were patented.

  Another advantage of Matroska is that it can be used for streaming video : while it isn’t typically, the spec allows it. Note that I do not mean progressive download (a’la Youtube), but rather actual streaming, where the encoder is working in real-time. The only way to do this with MP4 is by sending “segments” of video, a very hacky approach in which one is effectively sending a bunch of small MP4 files in sequence. This approach is used by Microsoft’s Silverlight “Smooth Streaming”. Not only is this an ugly hack, but it’s unsuitable for low-latency video. This kind of hack is unnecessary for Matroska. One possible problem is that since almost nobody currently uses Matroska for live streaming purposes, very few existing Matroska implementations support what is necessary to play streamed Matroska files.

  I’m not quite sure why Google chose to rebrand Matroska ; “WebM” is a silly name and Matroska is already pretty well-recognized as a brand.

  The choice of Vorbis for audio is practically a no-brainer. Even ignoring the issue of patents, libvorbis is still the best general-purpose open source audio encoder. While AAC is generally better at very low bitrates, there aren’t any good open source AAC encoders : faac is worse than LAME and ffmpeg’s AAC encoder is even worse. Furthermore, faac is not free software ; it contains code from the non-free reference encoder. Combined with the patent issue, nobody expected Google to pick anything else.

  Addendum C : Summary for the lazy

  VP8, as a spec, should be a bit better than H.264 Baseline Profile and VC-1. It’s not even close to competitive with H.264 Main or High Profile. If Google is willing to revise the spec, this can probably be improved.

  VP8, as an encoder, is somewhere between Xvid and Microsoft’s VC-1 in terms of visual quality. This can definitely be improved a lot.

  VP8, as a decoder, decodes even slower than ffmpeg’s H.264. This probably can’t be improved that much ; VP8 as a whole is similar in complexity to H.264.

  With regard to patents, VP8 copies too much from H.264 for comfort, no matter whose word is behind the claim of being patent-free. This doesn’t mean that it’s sure to be covered by patents, but until Google can give us evidence as to why it isn’t, I would be cautious.

  VP8 is definitely better compression-wise than Theora and Dirac, so if its claim to being patent-free does stand up, it’s a big upgrade with regard to patent-free video formats.

  VP8 is not ready for prime-time ; the spec is a pile of copy-pasted C code and the encoder’s interface is lacking in features and buggy. They aren’t even ready to finalize the bitstream format, let alone switch the world over to VP8.

  With the lack of a real spec, the VP8 software basically is the spec–and with the spec being “final”, any bugs are now set in stone. Such bugs have already been found and Google has rejected fixes.

  Google made the right decision to pick Matroska and Vorbis for its HTML5 video proposal.

  29.76103

• The first in-depth technical analysis of VP8

    
  19 mai 2010, par Dark Shikari — VP8, google

  Back in my original post about Internet video, I made some initial comments on the hope that VP8 would solve the problems of web video by providing a supposed patent-free video format with significantly better compression than the current options of Theora and Dirac. Fortunately, it seems I was able to acquire access to the VP8 spec, software, and source a good few days before the official release and so was able to perform a detailed technical analysis in time for the official release.

  The questions I will try to answer here are :

  1. How good is VP8 ? Is the file format actually better than H.264 in terms of compression, and could a good VP8 encoder beat x264 ? On2 claimed 50% better than H.264, but On2 has always made absurd claims that they were never able to back up with results, so such a number is almost surely wrong. VP7, for example, was claimed to be 15% better than H.264 while being much faster, but was in reality neither faster nor higher quality.

  2. How good is On2′s VP8 implementation ? Irrespective of how good the spec is, is the implementation good, or is this going to be just like VP3, where On2 releases an unusably bad implementation with the hope that the community will fix it for them ? Let’s hope not ; it took 6 years to fix Theora !

  3. How likely is VP8 to actually be free of patents ? Even if VP8 is worse than H.264, being patent-free is still a useful attribute for obvious reasons. But as noted in my previous post, merely being published by Google doesn’t guarantee that it is. Microsoft did similar a few years ago with the release of VC-1, which was claimed to be patent-free — but within mere months after release, a whole bunch of companies claimed patents on it and soon enough a patent pool was formed.

  We’ll start by going through the core features of VP8. We’ll primarily analyze them by comparing to existing video formats. Keep in mind that an encoder and a spec are two different things : it’s possible for good encoder to be written for a bad spec or vice versa ! Hence why a really good MPEG-1 encoder can beat a horrific H.264 encoder.

  But first, a comment on the spec itself.

  AAAAAAAGGGGGGGGGGGGGHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH !

  The spec consists largely of C code copy-pasted from the VP8 source code — up to and including TODOs, “optimizations”, and even C-specific hacks, such as workarounds for the undefined behavior of signed right shift on negative numbers. In many places it is simply outright opaque. Copy-pasted C code is not a spec. I may have complained about the H.264 spec being overly verbose, but at least it’s precise. The VP8 spec, by comparison, is imprecise, unclear, and overly short, leaving many portions of the format very vaguely explained. Some parts even explicitly refuse to fully explain a particular feature, pointing to highly-optimized, nigh-impossible-to-understand reference code for an explanation. There’s no way in hell anyone could write a decoder solely with this spec alone.

  Now that I’ve gotten that out of my system, let’s get back to VP8 itself. To begin with, to get a general sense for where all this fits in, basically all modern video formats work via some variation on the following chain of steps :

  Encode : Predict -> Transform + Quant -> Entropy Code -> Loopfilter
  Decode : Entropy Decode -> Predict -> Dequant + Inverse Transform -> Loopfilter

  If you’re looking to just get to the results and skip the gritty technical details, make sure to check out the “overall verdict” section and the “visual results” section. Or at least skip to the “summary for the lazy”.
  

  Prediction

  Prediction is any step which attempts to guess the content of an area of the frame. This could include functions based on already-known pixels in the same frame (e.g. inpainting) or motion compensation from a previous frame. Prediction usually involves side data, such as a signal telling the decoder a motion vector to use for said motion compensation.

  Intra Prediction

  Intra prediction is used to guess the content of a block without referring to other frames. VP8′s intra prediction is basically ripped off wholesale from H.264 : the “subblock” prediction modes are almost exactly identical (they even have the same names !) to H.264′s i4x4 mode, and the whole block prediction mode is basically identical to i16x16. Chroma prediction modes are practically identical as well. i8x8, from H.264 High Profile, is not present. An additional difference is that the planar prediction mode has been replaced with TM_PRED, a very vaguely similar analogue. The specific prediction modes are internally slightly different, but have the same names as in H.264.

  Honestly, I’m very disappointed here. While H.264′s intra prediction is good, it has certainly been improved on quite a bit over the past 7 years, and I thought that blatantly ripping it off was the domain of companies like Real (see RV40). I expected at least something slightly more creative out of On2. But more important than any of that : this is a patent time-bomb waiting to happen. H.264′s spatial intra prediction is covered in patents and I don’t think that On2 will be able to just get away with changing the rounding in the prediction modes. I’d like to see Google’s justification for this — they must have a good explanation for why they think there won’t be any patent issues.

  Update : spatial intra prediction apparently dates back to Nokia’s MVC H.26L proposal, from around 2000. It’s possible that Google believes that this is sufficient prior art to invalidate existing patents — which is not at all unreasonable !

  Verdict on Intra Prediction : Slightly modified ripoff of H.264. Somewhat worse than H.264 due to omission of i8x8.

  Inter Prediction

  Inter prediction is used to guess the content of a block by referring to past frames. There are two primary components to inter prediction : reference frames and motion vectors. The reference frame is a past frame from which to grab pixels from and the motion vectors index an offset into that frame. VP8 supports a total of 3 reference frames : the previous frame, the “alt ref” frame, and the “golden frame”. For motion vectors, VP8 supports variable-size partitions much like H.264. For subpixel precision, it supports quarter-pel motion vectors with a 6-tap interpolation filter. In short :

  VP8 reference frames : up to 3
  H.264 reference frames : up to 16
  VP8 partition types : 16×16, 16×8, 8×16, 8×8, 4×4
  H.264 partition types : 16×16, 16×8, 8×16, flexible subpartitions (each 8×8 can be 8×8, 8×4, 4×8, or 4×4).
  VP8 chroma MV derivation : each 4×4 chroma block uses the average of colocated luma MVs (same as MPEG-4 ASP)
  H.264 chroma MV derivation : chroma uses luma MVs directly
  VP8 interpolation filter : qpel, 6-tap luma, mixed 4/6-tap chroma
  H.264 interpolation filter : qpel, 6-tap luma (staged filter), bilinear chroma
  H.264 has but VP8 doesn’t : B-frames, weighted prediction

  H.264 has a significantly better and more flexible referencing structure. Sub-8×8 partitions are mostly unnecessary, so VP8′s omission of the H.264-style subpartitions has little consequence. The chroma MV derivation is more accurate in H.264 but slightly slower ; in practice the difference is probably near-zero both speed and compression-wise, since sub-8×8 luma partitions are rarely used (and I would suspect the same carries over to VP8).

  The VP8 interpolation filter is likely slightly better, but will definitely be slower to implement, both encoder and decoder-side. A staged filter allows the encoder to precalculate all possible halfpel positions and then quickly calculate qpel positions when necessary : an unstaged filter does not, making subpel motion estimation much slower. Not that unstaged filters are bad — staged filters have basically been abandoned for all of the H.265 proposals — it’s just an inherent disadvantage performance-wise. Additionally, having as high as 6 taps on chroma is, IMO, completely unnecessary and wasteful.

  The lack of B-frames in VP8 is a killer. B-frames can give 10-20% (or more) compression benefit for minimal speed cost ; their omission in VP8 probably costs more compression than all other problems noted in this post combined. This was not unexpected, however ; On2 has never used B-frames in any of their video formats. They also likely present serious patent problems, which probably explains their omission. Lack of weighted prediction is also going to hurt a bit, especially in fades.

  Update : Alt-ref frames can apparently be used to partially replicate the lack of B-frames. It’s not nearly as good, but it can get at least some of the benefit without actual B-frames.

  Verdict on Inter Prediction : Similar partitioning structure to H.264. Much weaker referencing structure. More complex, slightly better interpolation filter. Mostly a wash — except for the lack of B-frames, which is seriously going to hurt compression.
  

  Transform and Quantization
  

  After prediction, the encoder takes the difference between the prediction and the actual source pixels (the residual), transforms it, and quantizes it. The transform step is designed to make the data more amenable to compression by decorrelating it. The quantization step is the actual information-losing step where compression occurs ; the output values of transform are rounded, mostly to zero, leaving only a few integer coefficients.

  Transform

  For transform, VP8 uses again a very H.264-reminiscent scheme. Each 16×16 macroblock is divided into 16 4×4 DCT blocks, each of which is transformed by a bit-exact DCT approximation. Then, the DC coefficients of each block are collected into another 4×4 group, which is then Hadamard-transformed. OK, so this isn’t reminiscent of H.264, this is H.264. There are, however, 3 differences between VP8′s scheme and H.264′s.

  The first is that the 8×8 transform is omitted entirely (fitting with the omission of the i8x8 intra mode). The second is the specifics of the transform itself. H.264 uses an extremely simplified “DCT” which is so un-DCT-like that it often referred to as the HCT (H.264 Cosine Transform) instead. This simplified transform results in roughly 1% worse compression, but greatly simplifies the transform itself, which can be implemented entirely with adds, subtracts, and right shifts by 1. VC-1 uses a more accurate version that relies on a few small multiplies (numbers like 17, 22, 10, etc). VP8 uses an extremely, needlessly accurate version that uses very large multiplies (20091 and 35468). This in retrospect is not surpising, as it is very similar to what VP3 used.

  The third difference is that the Hadamard hierarchical transform is applied for some inter blocks, not merely i16x16. In particular, it also runs for p16x16 blocks. While this is definitely a good idea, especially given the small transform size (and the need to decorrelate the DC value between the small transforms), I’m not quite sure I agree with the decision to limit it to p16x16 blocks ; it seems that perhaps with a small amount of modification this could also be useful for other motion partitions. Also, note that unlike H.264, the hierarchical transform is luma-only and not applied to chroma.

  Overall, the transform scheme in VP8 is definitely weaker than in H.264. The lack of an 8×8 transform is going to have a significant impact on detail retention, especially at high resolutions. The transform is needlessly slower than necessary as well, though a shift-based transform might be out of the question due to patents. The one good new idea here is applying the hierarchical DC transform to inter blocks.

  Verdict on Transform : Similar to H.264. Slower, slightly more accurate 4×4 transform. Improved DC transform for luma (but not on chroma). No 8×8 transform. Overall, worse.

  Quantization

  For quantization, the core process is basically the same among all MPEG-like video formats, and VP8 is no exception. The primary ways that video formats tend to differentiate themselves here is by varying quantization scaling factors. There are two ways in which this is primarily done : frame-based offsets that apply to all coefficients or just some portion of them, and macroblock-level offsets. VP8 primarily uses the former ; in a scheme much less flexible than H.264′s custom quantization matrices, it allows for adjusting the quantizer of luma DC, luma AC, chroma DC, and so forth, separately. The latter (macroblock-level quantizer choice) can, in theory, be done using its “segmentation map” features, albeit very hackily and not very efficiently.

  The killer mistake that VP8 has made here is not making macroblock-level quantization a core feature of VP8. Algorithms that take advantage of macroblock-level quantization are known as “adaptive quantization” and are absolutely critical to competitive visual quality. My implementation of variance-based adaptive quantization (before, after) in x264 still stands to this day as the single largest visual quality gain in x264 history. Encoder comparisons have showed over and over that encoders without adaptive quantization simply cannot compete.

  Thus, while adaptive quantization is possible in VP8, the only way to implement it is to define one segment map for every single quantizer that one wants and to code the segment map index for every macroblock. This is inefficient and cumbersome ; even the relatively suboptimal MPEG-style delta quantizer system would be a better option. Furthermore, only 4 segment maps are allowed, for a maximum of 4 quantizers per frame.

  Verdict on Quantization : Lack of well-integrated adaptive quantization is going to be a killer when the time comes to implement psy optimizations. Overall, much worse.

  Entropy Coding

  Entropy coding is the process of taking all the information from all the other processes : DCT coefficients, prediction modes, motion vectors, and so forth — and compressing them losslessly into the final output file. VP8 uses an arithmetic coder somewhat similar to H.264′s, but with a few critical differences. First, it omits the range/probability table in favor of a multiplication. Second, it is entirely non-adaptive : unlike H.264′s, which adapts after every bit decoded, probability values are constant over the course of the frame. Accordingly, the encoder may periodically send updated probability values in frame headers for some syntax elements. Keyframes reset the probability values to the defaults.

  This approach isn’t surprising ; VP5 and VP6 (and probably VP7) also used non-adaptive arithmetic coders. How much of a penalty this actually means compression-wise is unknown ; it’s not easy to measure given the design of either H.264 or VP8. More importantly, I question the reason for this : making it adaptive would add just one single table lookup to the arithmetic decoding function — hardly a very large performance impact.

  Of course, the arithmetic coder is not the only part of entropy coding : an arithmetic coder merely turns 0s and 1s into an output bitstream. The process of creating those 0s and 1s and selecting the probabilities for the encoder to use is an equally interesting problem. Since this is a very complicated part of the video format, I’ll just comment on the parts that I found particularly notable.

  Motion vector coding consists of two parts : prediction based on neighboring motion vectors and the actual compression of the resulting delta between that and the actual motion vector. The prediction scheme in VP8 is a bit odd — worse, the section of the spec covering this contains no English explanation, just confusingly-written C code. As far as I can tell, it chooses an arithmetic coding context based on the neighboring MVs, then decides which of the predicted motion vectors to use, or whether to code a delta instead.

  The downside of this scheme is that, like in VP3/Theora (though not nearly as badly), it biases heavily towards the re-use of previous motion vectors. This is dangerous because, as the Theora devs have recently found (and fixed to some extent in Theora 1.2 aka Ptalabvorm), any situation in which the encoder picks a motion vector which isn’t the “real” motion vector in order to save bits can potentially have negative visual consequences. In terms of raw efficiency, I’m not sure whether VP8 or H.264′s prediction is better here.

  The compression of the resulting delta is similar to H.264, except for the coding of very large deltas, which is slightly better (similar to FFV1′s Golomb-like arithmetic codes).

  Intra prediction mode coding is done using arithmetic coding contexts based on the modes of the neighboring blocks. This is probably a good bit better than the hackneyed method that H.264 uses, which always struck me as being poorly designed.

  Residual coding is even more difficult to understand than motion vector coding, as the only full reference is a bunch of highly optimized, highly obfuscated C code. Like H.264′s CAVLC, it bases contexts on the number of nonzero coefficients in the top and left blocks relative to the current block. In addition, it also considers the magnitude of those coefficients and, like H.264′s CABAC, updates as coefficients are decoded.

  One more thing to note is the data partitioning scheme used by VP8. This scheme is much like VP3/Theora’s and involves putting each syntax element in its own component of the bitstream. The unfortunate problem with this is that it’s a nightmare for hardware implementations, greatly increasing memory bandwidth requirements. I have already received a complaint from a hardware developer about this specific feature with regard to VP8.

  Verdict on Entropy Coding : I’m not quite sure here. It’s better in some ways, worse in some ways, and just plain weird in others. My hunch is that it’s probably a very slight win for H.264 ; non-adaptive arithmetic coding has to have some serious penalties. It may also be a hardware implementation problem.
  

  Loop Filter

  The loop filter is run after decoding or encoding a frame and serves to perform extra processing on a frame, usually to remove blockiness in DCT-based video formats. Unlike postprocessing, this is not only for visual reasons, but also to improve prediction for future frames. Thus, it has to be done identically in both the encoder and decoder. VP8′s loop filter is vaguely similar to H.264′s, but with a few differences. First, it has two modes (which can be chosen by the encoder) : a fast mode and a normal mode. The fast mode is somewhat simpler than H.264′s, while the normal mode is somewhat more complex. Secondly, when filtering between macroblocks, VP8′s filter has wider range than the in-macroblock filter — H.264 did this, but only for intra edges.

  Third, VP8′s filter omits most of the adaptive strength mechanics inherent in H.264′s filter. Its only adaptation is that it skips filtering on p16x16 blocks with no coefficients. This may be responsible for the high blurriness of VP8′s loop filter : it will run over and over and over again on all parts of a macroblock even if they are unchanged between frames (as long as some other part of the macroblock is changed). H.264′s, by comparison, is strength-adaptive based on whether DCT coefficients exist on either side of a given edge and based on the motion vector delta and reference frame delta across said edge. Of course, skipping this strength calculation saves some decoding time as well.

  Update :
  05:28 < derf> Gumboot : You’ll be disappointed to know they got the loop filter ordering wrong again.
  05:29 < derf> Dark_Shikari : They ordered it such that you have to process each macroblock in full before processing the next one.

  Verdict on Loop Filter : Definitely worse compression-wise than H.264′s due to the lack of adaptive strength. Especially with the “fast” mode, might be significantly faster. I worry about it being too blurry.

  Overall verdict on the VP8 video format

  Overall, VP8 appears to be significantly weaker than H.264 compression-wise. The primary weaknesses mentioned above are the lack of proper adaptive quantization, lack of B-frames, lack of an 8×8 transform, and non-adaptive loop filter. With this in mind, I expect VP8 to be more comparable to VC-1 or H.264 Baseline Profile than with H.264. Of course, this is still significantly better than Theora, and in my tests it beats Dirac quite handily as well.

  Supposedly Google is open to improving the bitstream format — but this seems to conflict with the fact that they got so many different companies to announce VP8 support. The more software that supports a file format, the harder it is to change said format, so I’m dubious of any claim that we will be able to spend the next 6-12 months revising VP8. In short, it seems to have been released too early : it would have been better off to have an initial period during which revisions could be submitted and then a big announcement later when it’s completed.

  Update : it seems that Google is not open to changing the spec : it is apparently “final”, complete with all its flaws.

  In terms of decoding speed I’m not quite sure ; the current implementation appears to be about 16% slower than ffmpeg’s H.264 decoder (and thus probably about 25-35% slower than state-of-the-art decoders like CoreAVC). Of course, this doesn’t necessarily say too much about what a fully optimized implementation will reach, but the current one seems to be reasonably well-optimized and has SIMD assembly code for almost all major DSP functions, so I doubt it will get that much faster.

  I would expect, with equally optimized implementations, VP8 and H.264 to be relatively comparable in terms of decoding speed. This, of course, is not really a plus for VP8 : H.264 has a great deal of hardware support, while VP8 largely has to rely on software decoders, so being “just as fast” is in many ways not good enough. By comparison, Theora decodes almost 35% faster than H.264 using ffmpeg’s decoder.

  Finally, the problem of patents appears to be rearing its ugly head again. VP8 is simply way too similar to H.264 : a pithy, if slightly inaccurate, description of VP8 would be “H.264 Baseline Profile with a better entropy coder”. Even VC-1 differed more from H.264 than VP8 does, and even VC-1 didn’t manage to escape the clutches of software patents. It’s quite possible that VP8 has no patent issues, but until we get some hard evidence that VP8 is safe, I would be cautious. Since Google is not indemnifying users of VP8 from patent lawsuits, this is even more of a potential problem. Most importantly, Google has not released any justifications for why the various parts of VP8 do not violate patents, as Sun did with their OMS standard : such information would certainly cut down on speculation and make it more clear what their position actually is.

  But if luck is on Google’s side and VP8 does pass through the patent gauntlet unscathed, it will undoubtedly be a major upgrade as compared to Theora.

  Addendum A : On2′s VP8 Encoder and Decoder

  This post is primarily aimed at discussing issues relating to the VP8 video format. But from a practical perspective, while software can be rewritten and improved, to someone looking to use VP8 in the near future, the quality (both code-wise, compression-wise, and speed-wise) of the official VP8 encoder and decoder is more important than anything I’ve said above. Thus, after reading through most of the code, here’s my thoughts on the software.

  Initially I was intending to go easy on On2 here ; I assumed that this encoder was in fact new for VP8 and thus they wouldn’t necessarily have time to make the code high-quality and improve its algorithms. However, as I read through the encoder, it became clear that this was not at all true ; there were comments describing bugfixes dating as far back as early 2004. That’s right : this software is even older than x264 ! I’m guessing that the current VP8 software simply evolved from the original VP7 software. Anyways, this means that I’m not going to go easy on On2 ; they’ve had (at least) 6 years to work on VP8, and a much larger dev team than x264′s to boot.

  Before I tear the encoder apart, keep in mind that it isn’t bad. In fact, compression-wise, I don’t think they’re going to be able to get it that much better using standard methods. I would guess that the encoder, on slowest settings, is within 5-10% of the maximum PSNR that they’ll ever get out of it. There’s definitely a whole lot more to be had using unusual algorithms like MB-tree, not to mention the complete lack of psy optimizations — but at what it tries to do, it does pretty decently. This is in contrast to the VP3 encoder, which was a pile of garbage (just ask any Theora dev).

  Before I go into specific components, a general note on code quality. The code quality is much better than VP3, though there’s still tons of typos in the comments. They also appear to be using comments as a form of version control system, which is a bit bizarre. The assembly code is much worse, with staggering levels of copy-paste coding, some completely useless instructions that do nothing at all, unaligned loads/stores to what-should-be aligned data structures, and a few functions that are simply written in unfathomably roundabout (and slower) ways. While the C code isn’t half bad, the assembly is clearly written by retarded monkeys. But I’m being unfair : this is way better than with VP3.

  Motion estimation : Diamond, hex, and exhaustive (full) searches available. All are pretty naively implemented : hexagon, for example, performs a staggering amount of redundant work (almost half of the locations it searches are repeated !). Full is even worse in terms of inefficiency, but it’s useless for all but placebo-level speeds, so I’m not really going to complain about that.

  Subpixel motion estimation : Straightforward iterative diamond and square searches. Nothing particularly interesting here.

  Quantization : Primary quantization has two modes : a fast mode and a slightly slower mode. The former is just straightforward deadzone quant, while the latter has a bias based on zero-run length (not quite sure how much this helps, but I like the idea). After this they have “coefficient optimization” with two modes. One mode simply tries moving each nonzero coefficient towards zero ; the slow mode tries all 2^16 possible DCT coefficient rounding permutations. Whoever wrote this needs to learn what trellis quantization (the dynamic programming solution to the problem) is and stop using exponential-time algorithms in encoders.

  Ratecontrol (frame type handling) : Relies on “boosting” the quality of golden frames and “alt-ref” frames — a concept I find extraordinarily dubious because it means that the video will periodically “jump” to a higher quality level, which looks utterly terrible in practice. You can see the effect in this graph of PSNR ; every dozen frames or so, the quality “jumps”. This cannot possibly look good in motion.

  Ratecontrol (overall) : Relies on a purely reactive ratecontrol algorithm, which probably will not do very well in difficult situations such as hard-CBR and tight buffer constraints. Furthermore, it does no adaptation of the quantizer within the frame (e.g. in the case that the frame overshot the size limitations ratecontrol put on it). Instead, it relies on re-encoding the frame repeatedly to reach the target size — which in practice is simply not a usable option for two reasons. In low-latency situations where one can’t have a large delay, re-encoding repeatedly may send the encoder way behind time-wise. In any other situation, one can afford to use frame-based threading, a much faster algorithm for multithreaded encoding than the typical slice-based threading — which makes re-encoding impossible.

  Loop filter : The encoder attempts to optimize the loop filter parameters for maximum PSNR. I’m not quite sure how good an idea this is ; every example I’ve seen of this with H.264 ends up creating very bad (often blurry) visual results.

  Overall performance : Even on the absolute fastest settings with multithreading, their encoder is slow. On my 1.6Ghz Core i7 it gets barely 26fps encoding 1080p ; not even enough to reliably do real-time compression. x264, by comparison, gets 101fps at its fastest preset “ultrafast”. Now, sure, I don’t expect On2′s encoder to be anywhere near as fast as x264, but being unable to stream HD video on a modern quad-core system is simply not reasonable in 2010. Additionally, the speed options are extraordinarily confusing and counterintuitive and don’t always seem to work properly ; for example, fast encoding mode (–rt) seems to be ignored completely in 2-pass.

  Overall compression : As said before, compression-wise the encoder does a pretty good job with the spec that it’s given. The slower algorithms in the encoder are clearly horrifically unoptimized (see the comments on motion search and quantization in particular), but they still work.

  Decoder : Seems to be straightforward enough. Nothing jumped out at me as particularly bad, slow, or otherwise, besides the code quality issues mentioned above.

  Practical problems : The encoder and decoder share a staggering amount of code. This means that any bug in the common code will affect both, and thus won’t be spotted because it will affect them both in a matching fashion.  This is the inherent problem with any file format that doesn’t have independent implementations and is defined by a piece of software instead of a spec : there are always bugs. RV40 had a hilarious example of this, where a typo of “22″ instead of “33″ resulted in quarter-pixel motion compensation being broken. Accordingly, I am very dubious of any file format defined by software instead of a specification. Google should wait until independent implementations have been created before setting the spec in stone.

  Update : it seems that what I forsaw is already coming true :

  <derf> gmaxwell : It survives it with a patch that causes artifacts because their encoder doesn’t clamp MVs properly.
  <gmaxwell> ::cries: :
  <derf> So they reverted my decoder patch, instead of fixing the encoder.
  <gmaxwell> “but we have many files encoded with this !”
  <gmaxwell> so great.. single implementation and it depends on its own bugs.

  This is just like Internet Explorer 6 all over again — bugs in the software become part of the “spec” !

  Hard PSNR numbers :
  (Source/target bitrate are the same as in my upcoming comparison.)
  x264, slowest mode, High Profile : 29.76103db ( 28% better than VP8)
  VP8, slowest mode : 28.37708db ( 8.5% better than x264 baseline)
  x264, slowest mode, Baseline Profile : 27.95594db

  Note that these numbers are a “best-case” situation : we’re testing all three optimized for PSNR, which is what the current VP8 encoder specializes in as well. This is not too different from my expectations above as estimated from the spec itself ; it’s relatively close to x264′s Baseline Profile.

  Keep in mind that this is not representative of what you can get out of VP8 now, but rather what could be gotten out of VP8. PSNR is meaningless for real-world encoding — what matters is visual quality — so hopefully if problems like the adaptive quantization issue mentioned previously can be overcome, the VP8 encoder could be improved to have x264-level psy optimizations. However, as things stand…

  Visual results : Unfortunately, since the current VP8 encoder optimizes entirely for PSNR, the visual results are less than impressive. Here’s a sampling of how it compares with some other encoders. Source and bitrate are the same as above ; all encoders are optimized for optimal visual quality wherever possible. And apparently given some of the responses to this part, many people cannot actually read ; the bitrate is (as close as possible to) the same on all of these files.

  Update : I got completely slashdotted and my few hundred gigs of bandwidth ran out in mere hours. The images below have been rehosted, so if you’ve pasted the link somewhere else, check below for the new one.

  VP8 (On2 VP8 rc8) (source) (Note : I recently realized that the official encoder doesn’t output MKV, so despite the name, this file is actually a VP8 bitstream wrapped in IVF, as generated by ivfenc. Decode it with ivfdec.)
  H.264 (Recent x264) (source)
  H.264 Baseline Profile (Recent x264) (source)
  Theora (Recent ptalabvorm nightly) (source)
  Dirac (Schroedinger 1.0.9) (source)
  VC-1 (Microsoft VC-1 SDK) (source)
  MPEG-4 ASP (Xvid 1.2.2) (source)

  The quality generated by On2′s VP8 encoder will probably not improve significantly without serious psy optimizations.

  One further note about the encoder : currently it will drop frames by default, which is incredibly aggravating and may cause serious problems. I strongly suggest anyone using it to turn the frame-dropping feature off in the options.

  Addendum B : Google’s choice of container and audio format for HTML5

  Google has chosen Matroska for their container format. This isn’t particularly surprising : Matroska is one of the most widely used “modern” container formats and is in many ways best-suited to the task. MP4 (aka ISOmedia) is probably a better-designed format, but is not very flexible ; while in theory it can stick anything in a private stream, a standardization process is technically necessary to “officially” support any new video or audio formats. Patents are probably a non-issue ; the MP4 patent pool was recently disbanded, largely because nobody used any of the features that were patented.

  Another advantage of Matroska is that it can be used for streaming video : while it isn’t typically, the spec allows it. Note that I do not mean progressive download (a’la Youtube), but rather actual streaming, where the encoder is working in real-time. The only way to do this with MP4 is by sending “segments” of video, a very hacky approach in which one is effectively sending a bunch of small MP4 files in sequence. This approach is used by Microsoft’s Silverlight “Smooth Streaming”. Not only is this an ugly hack, but it’s unsuitable for low-latency video. This kind of hack is unnecessary for Matroska. One possible problem is that since almost nobody currently uses Matroska for live streaming purposes, very few existing Matroska implementations support what is necessary to play streamed Matroska files.

  I’m not quite sure why Google chose to rebrand Matroska ; “WebM” is a silly name and Matroska is already pretty well-recognized as a brand.

  The choice of Vorbis for audio is practically a no-brainer. Even ignoring the issue of patents, libvorbis is still the best general-purpose open source audio encoder. While AAC is generally better at very low bitrates, there aren’t any good open source AAC encoders : faac is worse than LAME and ffmpeg’s AAC encoder is even worse. Furthermore, faac is not free software ; it contains code from the non-free reference encoder. Combined with the patent issue, nobody expected Google to pick anything else.

  Addendum C : Summary for the lazy

  VP8, as a spec, should be a bit better than H.264 Baseline Profile and VC-1. It’s not even close to competitive with H.264 Main or High Profile. If Google is willing to revise the spec, this can probably be improved.

  VP8, as an encoder, is somewhere between Xvid and Microsoft’s VC-1 in terms of visual quality. This can definitely be improved a lot.

  VP8, as a decoder, decodes even slower than ffmpeg’s H.264. This probably can’t be improved that much ; VP8 as a whole is similar in complexity to H.264.

  With regard to patents, VP8 copies too much from H.264 for comfort, no matter whose word is behind the claim of being patent-free. This doesn’t mean that it’s sure to be covered by patents, but until Google can give us evidence as to why it isn’t, I would be cautious.

  VP8 is definitely better compression-wise than Theora and Dirac, so if its claim to being patent-free does stand up, it’s a big upgrade with regard to patent-free video formats.

  VP8 is not ready for prime-time ; the spec is a pile of copy-pasted C code and the encoder’s interface is lacking in features and buggy. They aren’t even ready to finalize the bitstream format, let alone switch the world over to VP8.

  With the lack of a real spec, the VP8 software basically is the spec–and with the spec being “final”, any bugs are now set in stone. Such bugs have already been found and Google has rejected fixes.

  Google made the right decision to pick Matroska and Vorbis for its HTML5 video proposal.

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