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• Anatomy of an optimization : H.264 deblocking

  26 mai 2010, par Dark Shikari — H.264, assembly, development, speed, x264

  As mentioned in the previous post, H.264 has an adaptive deblocking filter. But what exactly does that mean — and more importantly, what does it mean for performance ? And how can we make it as fast as possible ? In this post I’ll try to answer these questions, particularly in relation to my recent deblocking optimizations in x264.

  H.264′s deblocking filter has two steps : strength calculation and the actual filter. The first step calculates the parameters for the second step. The filter runs on all the edges in each macroblock. That’s 4 vertical edges of length 16 pixels and 4 horizontal edges of length 16 pixels. The vertical edges are filtered first, from left to right, then the horizontal edges, from top to bottom (order matters !). The leftmost edge is the one between the current macroblock and the left macroblock, while the topmost edge is the one between the current macroblock and the top macroblock.

  Here’s the formula for the strength calculation in progressive mode. The highest strength that applies is always selected.

  If we’re on the edge between an intra macroblock and any other macroblock : Strength 4
  If we’re on an internal edge of an intra macroblock : Strength 3
  If either side of a 4-pixel-long edge has residual data : Strength 2
  If the motion vectors on opposite sides of a 4-pixel-long edge are at least a pixel apart (in either x or y direction) or the reference frames aren’t the same : Strength 1
  Otherwise : Strength 0 (no deblocking)

  These values are then thrown into a lookup table depending on the quantizer : higher quantizers have stronger deblocking. Then the actual filter is run with the appropriate parameters. Note that Strength 4 is actually a special deblocking mode that performs a much stronger filter and affects more pixels.

  One can see somewhat intuitively why these strengths are chosen. The deblocker exists to get rid of sharp edges caused by the block-based nature of H.264, and so the strength depends on what exists that might cause such sharp edges. The strength calculation is a way to use existing data from the video stream to make better decisions during the deblocking process, improving compression and quality.

  Both the strength calculation and the actual filter (not described here) are very complex if naively implemented. The latter can be SIMD’d with not too much difficulty ; no H.264 decoder can get away with reasonable performance without such a thing. But what about optimizing the strength calculation ? A quick analysis shows that this can be beneficial as well.

  Since we have to check both horizontal and vertical edges, we have to check up to 32 pairs of coefficient counts (for residual), 16 pairs of reference frame indices, and 128 motion vector values (counting x and y as separate values). This is a lot of calculation ; a naive implementation can take 500-1000 clock cycles on a modern CPU. Of course, there’s a lot of shortcuts we can take. Here’s some examples :

  • If the macroblock uses the 8×8 transform, we only need to check 2 edges in each direction instead of 4, because we don’t deblock inside of the 8×8 blocks.
  • If the macroblock is a P-skip, we only have to check the first edge in each direction, since there’s guaranteed to be no motion vector differences, reference frame differences, or residual inside of the macroblock.
  • If the macroblock has no residual at all, we can skip that check.
  • If we know the partition type of the macroblock, we can do motion vector checks only along the edges of the partitions.
  • If the effective quantizer is so low that no deblocking would be performed no matter what, don’t bother calculating the strength.

  But even all of this doesn’t save us from ourselves. We still have to iterate over a ton of edges, checking each one. Stuff like the partition-checking logic greatly complicates the code and adds overhead even as it reduces the number of checks. And in many cases decoupling the checks to add such logic will make it slower : if the checks are coupled, we can avoid doing a motion vector check if there’s residual, since Strength 2 overrides Strength 1.

  But wait. What if we could do this in SIMD, just like the actual loopfilter itself ? Sure, it seems more of a problem for C code than assembly, but there aren’t any obvious things in the way. Many years ago, Loren Merritt (pengvado) wrote the first SIMD implementation that I know of (for ffmpeg’s decoder) ; it is quite fast, so I decided to work on porting the idea to x264 to see if we could eke out a bit more speed here as well.

  Before I go over what I had to do to make this change, let me first describe how deblocking is implemented in x264. Since the filter is a loopfilter, it acts “in loop” and must be done in both the encoder and decoder — hence why x264 has it too, not just decoders. At the end of encoding one row of macroblocks, x264 goes back and deblocks the row, then performs half-pixel interpolation for use in encoding the next frame.

  We do it per-row for reasons of cache coherency : deblocking accesses a lot of pixels and a lot of code that wouldn’t otherwise be used, so it’s more efficient to do it in a single pass as opposed to deblocking each macroblock immediately after encoding. Then half-pixel interpolation can immediately re-use the resulting data.

  Now to the change. First, I modified deblocking to implement a subset of the macroblock_cache_load function : spend an extra bit of effort loading the necessary data into a data structure which is much simpler to address — as an assembly implementation would need (x264_macroblock_cache_load_deblock). Then I massively cleaned up deblocking to move all of the core strength-calculation logic into a single, small function that could be converted to assembly (deblock_strength_c). Finally, I wrote the assembly functions and worked with Loren to optimize them. Here’s the result.

  And the timings for the resulting assembly function on my Core i7, in cycles :

  deblock_strength_c : 309
  deblock_strength_mmx : 79
  deblock_strength_sse2 : 37
  deblock_strength_ssse3 : 33

  Now that is a seriously nice improvement. 33 cycles on average to perform that many comparisons–that’s absurdly low, especially considering the SIMD takes no branchy shortcuts : it always checks every single edge ! I walked over to my performance chart and happily crossed off a box.

  But I had a hunch that I could do better. Remember, as mentioned earlier, we’re reloading all that data back into our data structures in order to address it. This isn’t that slow, but takes enough time to significantly cut down on the gain of the assembly code. And worse, less than a row ago, all this data was in the correct place to be used (when we just finished encoding the macroblock) ! But if we did the deblocking right after encoding each macroblock, the cache issues would make it too slow to be worth it (yes, I tested this). So I went back to other things, a bit annoyed that I couldn’t get the full benefit of the changes.

  Then, yesterday, I was talking with Pascal, a former Xvid dev and current video hacker over at Google, about various possible x264 optimizations. He had seen my deblocking changes and we discussed that a bit as well. Then two lines hit me like a pile of bricks :

  <_skal_> tried computing the strength at least ?
  <_skal_> while it’s fresh

  Why hadn’t I thought of that ? Do the strength calculation immediately after encoding each macroblock, save the result, and then go pick it up later for the main deblocking filter. Then we can use the data right there and then for strength calculation, but we don’t have to do the whole deblock process until later.

  I went and implemented it and, after working my way through a horde of bugs, eventually got a working implementation. A big catch was that of slices : deblocking normally acts between slices even though normal encoding does not, so I had to perform extra munging to get that to work. By midday today I was able to go cross yet another box off on the performance chart. And now it’s committed.

  Sometimes chatting for 10 minutes with another developer is enough to spot the idea that your brain somehow managed to miss for nearly a straight week.

  NB : the performance chart is on a specific test clip at a specific set of settings (super fast settings) relevant to the company I work at, so it isn’t accurate nor complete for, say, default settings.

  Update : Here’s a higher resolution version of the current chart, as requested in the comments.
  

  http://git.videolan.org/?p=x264.git ;a=commit ;h=b9ec3ea27812288485329ac7771143021cd4917b

• The problems with wavelets

  27 février 2010, par Dark Shikari — DCT, Dirac, Snow, psychovisual optimizations, wavelets

  I have periodically noted in this blog and elsewhere various problems with wavelet compression, but many readers have requested that I write a more detailed post about it, so here it is.

  Wavelets have been researched for quite some time as a replacement for the standard discrete cosine transform used in most modern video compression. Their methodology is basically opposite : each coefficient in a DCT represents a constant pattern applied to the whole block, while each coefficient in a wavelet transform represents a single, localized pattern applied to a section of the block. Accordingly, wavelet transforms are usually very large with the intention of taking advantage of large-scale redundancy in an image. DCTs are usually quite small and are intended to cover areas of roughly uniform patterns and complexity.

  Both are complete transforms, offering equally accurate frequency-domain representations of pixel data. I won’t go into the mathematical details of each here ; the real question is whether one offers better compression opportunities for real-world video.

  DCT transforms, though it isn’t mathematically required, are usually found as block transforms, handling a single sharp-edged block of data. Accordingly, they usually need a deblocking filter to smooth the edges between DCT blocks. Wavelet transforms typically overlap, avoiding such a need. But because wavelets don’t cover a sharp-edged block of data, they don’t compress well when the predicted data is in the form of blocks.

  Thus motion compensation is usually performed as overlapped-block motion compensation (OBMC), in which every pixel is calculated by performing the motion compensation of a number of blocks and averaging the result based on the distance of those blocks from the current pixel. Another option, which can be combined with OBMC, is “mesh MC“, where every pixel gets its own motion vector, which is a weighted average of the closest nearby motion vectors. The end result of either is the elimination of sharp edges between blocks and better prediction, at the cost of greatly increased CPU requirements. For an overlap factor of 2, it’s 4 times the amount of motion compensation, plus the averaging step. With mesh MC, it’s even worse, with SIMD optimizations becoming nearly impossible.

  At this point, it would seem wavelets would have pretty big advantages : when used with OBMC, they have better inter prediction, eliminate the need for deblocking, and take advantage of larger-scale correlations. Why then hasn’t everyone switched over to wavelets then ? Dirac and Snow offer modern implementations. Yet despite decades of research, wavelets have consistently disappointed for image and video compression. It turns out there are a lot of serious practical issues with wavelets, many of which are open problems.

  1. No known method exists for efficient intra coding. H.264′s spatial intra prediction is extraordinarily powerful, but relies on knowing the exact decoded pixels to the top and left of the current block. Since there is no such boundary in overlapped-wavelet coding, such prediction is impossible. Newer intra prediction methods, such as markov-chain intra prediction, also seem to require an H.264-like situation with exactly-known neighboring pixels. Intra coding in wavelets is in the same state that DCT intra coding was in 20 years ago : the best known method was to simply transform the block with no prediction at all besides DC. NB : as described by Pengvado in the comments, the switching between inter and intra coding is potentially even more costly than the inefficient intra coding.

  2. Mixing partition sizes has serious practical problems. Because the overlap between two motion partitions depends on the partitions’ size, mixing block sizes becomes quite difficult to define. While in H.264 an smaller partition always gives equal or better compression than a larger one when one ignores the extra overhead, it is actually possible for a larger partition to win when using OBMC due to the larger overlap. All of this makes both the problem of defining the result of mixed block sizes and making decisions about them very difficult.

  Both Snow and Dirac offer variable block size, but the overlap amount is constant ; larger blocks serve only to save bits on motion vectors, not offer better overlap characteristics.

  3. Lack of spatial adaptive quantization. As shown in x264 with VAQ, and correspondingly in HCEnc’s implementation and Theora’s recent implementation, spatial adaptive quantization has staggeringly impressive (before, after) effects on visual quality. Only Dirac seems to have such a feature, and the encoder doesn’t even use it. No other wavelet formats (Snow, JPEG2K, etc) seem to have such a feature. This results in serious blurring problems in areas with subtle texture (as in the comparison below).

  4. Wavelets don’t seem to code visual energy effectively. Remember that a single coefficient in a DCT represents a pattern which applies across an entire block : this makes it very easy to create apparent “detail” with a DCT. Furthermore, the sharp edges of DCT blocks, despite being an apparent weakness, often result in a “fake sharpness” that can actually improve the visual appearance of videos, as was seen with Xvid. Thus wavelet codecs have a tendency to look much blurrier than DCT-based codecs, but since PSNR likes blur, this is often seen as a benefit during video compression research. Some of the consequences of these factors can be seen in this comparison ; somewhat outdated and not general-case, but which very effectively shows the difference in how wavelets handle sharp edges and subtle textures.

  Another problem that periodically crops up is the visual aliasing that tends to be associated with wavelets at lower bitrates. Standard wavelets effectively consist of a recursive function that upscales the coefficients coded by the previous level by a factor of 2 and then adds a new set of coefficients. If the upscaling algorithm is naive — as it often is, for the sake of speed — the result can look quite ugly, as if parts of the image were coded at a lower resolution and then badly scaled up. Of course, it looks like that because they were coded at a lower resolution and then badly scaled up.

  JPEG2000 is a classic example of wavelet failure : despite having more advanced entropy coding, being designed much later than JPEG, being much more computationally intensive, and having much better PSNR, comparisons have consistently shown it to be visually worse than JPEG at sane filesizes. Here’s an example from Wikipedia. By comparison, H.264′s intra coding, when used for still image compression, can beat JPEG by a factor of 2 or more (I’ll make a post on this later). With the various advancements in DCT intra coding since H.264, I suspect that a state-of-the-art DCT compressor could win by an even larger factor.

  Despite the promised benefits of wavelets, a wavelet encoder even close to competitive with x264 has yet to be created. With some tests even showing Dirac losing to Theora in visual comparisons, it’s clear that many problems remain to be solved before wavelets can eliminate the ugliness of block-based transforms once and for all.

• IJG swings again, and misses

  1er février 2010, par Mans — Multimedia

  Earlier this month the IJG unleashed version 8 of its ubiquitous libjpeg library on the world. Eager to try out the “major breakthrough in image coding technology” promised in the README file accompanying v7, I downloaded the release. A glance at the README file suggests something major indeed is afoot :

  Version 8.0 is the first release of a new generation JPEG standard to overcome the limitations of the original JPEG specification.

  The text also hints at the existence of a document detailing these marvellous new features, and a Google search later a copy has found its way onto my monitor. As I read, however, my state of mind shifts from an initial excited curiosity, through bewilderment and disbelief, finally arriving at pure merriment.

  Already on the first page it becomes clear no new JPEG standard in fact exists. All we have is an unsolicited proposal sent to the ITU-T by members of the IJG. Realising that even the most brilliant of inventions must start off as mere proposals, I carry on reading. The summary informs me that I am about to witness the introduction of three extensions to the T.81 JPEG format :

  1. An alternative coefficient scan sequence for DCT coefficient serialization
  2. A SmartScale extension in the Start-Of-Scan (SOS) marker segment
  3. A Frame Offset definition in or in addition to the Start-Of-Frame (SOF) marker segment

  Together these three extensions will, it is promised, “bring DCT based JPEG back to the forefront of state-of-the-art image coding technologies.”

  Alternative scan

  The first of the proposed extensions introduces an alternative DCT coefficient scan sequence to be used in place of the zigzag scan employed in most block transform based codecs.

  

  Alternative scan sequence

  The advantage of this scan would be that combined with the existing progressive mode, it simplifies decoding of an initial low-resolution image which is enhanced through subsequent passes. The author of the document calls this scheme “image-pyramid/hierarchical multi-resolution coding.” It is not immediately obvious to me how this constitutes even a small advance in image coding technology.

  At this point I am beginning to suspect that our friend from the IJG has been trapped in a half-world between interlaced GIF images transmitted down noisy phone lines and today’s inferno of SVC, MVC, and other buzzwords.

  (Not so) SmartScale

  Disguised behind this camel-cased moniker we encounter a method which, we are told, will provide better image quality at high compression ratios. The author has combined two well-known (to us) properties in a (to him) clever way.

  The first property concerns the perceived impact of different types of distortion in an image. When encoding with JPEG, as the quantiser is increased, the decoded image becomes ever more blocky. At a certain point, a better subjective visual quality can be achieved by down-sampling the image before encoding it, thus allowing a lower quantiser to be used. If the decoded image is scaled back up to the original size, the unpleasant, blocky appearance is replaced with a smooth blur.

  The second property belongs to the DCT where, as we all know, the top-left (DC) coefficient is the average of the entire block, its neighbours represent the lowest frequency components etc. A top-left-aligned subset of the coefficient block thus represents a low-resolution version of the full block in the spatial domain.

  In his flash of genius, our hero came up with the idea of using the DCT for down-scaling the image. Unfortunately, he appears to possess precious little knowledge of sampling theory and human visual perception. Any block-based resampling will inevitably produce sharp artefacts along the block edges. The human visual system is particularly sensitive to sharp edges, so this is one of the most unwanted types of distortion in an encoded image.

  Despite the obvious flaws in this approach, I decided to give it a try. After all, the software is already written, allowing downscaling by factors of 8/8..16.

  Using a 1280×720 test image, I encoded it with each of the nine scaling options, from unity to half size, each time adjusting the quality parameter for a final encoded file size of no more than 200000 bytes. The following table presents the encoded file size, the libjpeg quality parameter used, and the SSIM metric for each of the images.

  Scale	Size	Quality	SSIM
  8/8	198462	59	0.940
  8/9	196337	70	0.936
  8/10	196133	79	0.934
  8/11	197179	84	0.927
  8/12	193872	89	0.915
  8/13	197153	92	0.914
  8/14	188334	94	0.899
  8/15	198911	96	0.886
  8/16	197190	97	0.869

  Although the smaller images allowed a higher quality setting to be used, the SSIM value drops significantly. Numbers may of course be misleading, but the images below speak for themselves. These are cut-outs from the full image, the original on the left, unscaled JPEG-compressed in the middle, and JPEG with 8/16 scaling to the right.

  

  

  Looking at these images, I do not need to hesitate before picking the JPEG variant I prefer.

  Frame offset

  The third and final extension proposed is quite simple and also quite pointless : a top-left cropping to be applied to the decoded image. The alleged utility of this feature would be to enable lossless cropping of a JPEG image. In a typical image workflow, however, JPEG is only used for the final published version, so the need for this feature appears quite far-fetched.

  The grand finale

  Throughout the text, the author makes references to “the fundamental DCT property for image representation.” In his own words :

  This property was found by the author during implementation of the new DCT scaling features and is after his belief one of the most important discoveries in digital image coding after releasing the JPEG standard in 1992.

  The secret is to be revealed in an annex to the main text. This annex quotes in full a post by the author to the comp.dsp Usenet group in a thread with the subject why DCT. Reading the entire thread proves quite amusing. A few excerpts follow.

  The actual reason is much simpler, and therefore apparently very difficult to recognize by complicated-thinking people.

  Here is the explanation :

  What are people doing when they have a bunch of images and want a quick preview ? They use thumbnails ! What are thumbnails ? Thumbnails are small downscaled versions of the original image ! If you want more details of the image, you can zoom in stepwise by enlarging (upscaling) the image.

  So with proper understanding of the fundamental DCT property, the MPEG folks could make their videos more scalable, but, as in the case of JPEG, they are unable to recognize this simple but basic property, unfortunately, and pursue rather inferior approaches in actual developments.

  These are just phrases, and they don’t explain anything. But this is typical for the current state in this field : The relevant people ignore and deny the true reasons, and thus they turn in a circle and no progress is being made.

  However, there are dark forces in action today which ignore and deny any fruitful advances in this field. That is the reason that we didn’t see any progress in JPEG for more than a decade, and as long as those forces dominate, we will see more confusion and less enlightenment. The truth is always simple, and the DCT *is* simple, but this fact is suppressed by established people who don’t want to lose their dubious position.

  I believe a trip to the Total Perspective Vortex may be in order. Perhaps his tin-foil hat will save him.