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• VP8 : a retrospective

  13 juillet 2010, par Dark Shikari — DCT, VP8, speed

  I’ve been working the past few weeks to help finish up the ffmpeg VP8 decoder, the first community implementation of On2′s VP8 video format. Now that I’ve written a thousand or two lines of assembly code and optimized a good bit of the C code, I’d like to look back at VP8 and comment on a variety of things — both good and bad — that slipped the net the first time, along with things that have changed since the time of that blog post.

  These are less-so issues related to compression — that issue has been beaten to death, particularly in MSU’s recent comparison, where x264 beat the crap out of VP8 and the VP8 developers pulled a Pinocchio in the developer comments. But that was expected and isn’t particularly interesting, so I won’t go into that. VP8 doesn’t have to be the best in the world in order to be useful.

  When the ffmpeg VP8 decoder is complete (just a few more asm functions to go), we’ll hopefully be able to post some benchmarks comparing it to libvpx.

  1. The spec, er, I mean, bitstream guide.

  Google has reneged on their claim that a spec existed at all and renamed it a “bitstream guide”. This is probably after it was found that — not merely was it incomplete — but at least a dozen places in the spec differed wildly from what was actually in their own encoder and decoder software ! The deblocking filter, motion vector clamping, probability tables, and many more parts simply disagreed flat-out with the spec. Fortunately, Ronald Bultje, one of the main authors of the ffmpeg VP8 decoder, is rather skilled at reverse-engineering, so we were able to put together a matching implementation regardless.

  Most of the differences aren’t particularly important — they don’t have a huge effect on compression or anything — but make it vastly more difficult to implement a “working” VP8 decoder, or for that matter, decide what “working” really is. For example, Google’s decoder will, if told to “swap the ALT and GOLDEN reference frames”, overwrite both with GOLDEN, because it first sets GOLDEN = ALT, and then sets ALT = GOLDEN. Is this a bug ? Or is this how it’s supposed to work ? It’s hard to tell — there isn’t a spec to say so. Google says that whatever libvpx does is right, but I doubt they intended this.

  I expect a spec will eventually be written, but it was a bit obnoxious of Google — both to the community and to their own developers — to release so early that they didn’t even have their own documentation ready.

  2. The TM intra prediction mode.

  One thing I glossed over in the original piece was that On2 had added an extra intra prediction mode to the standard batch that H.264 came with — they replaced Planar with “TM pred”. For i4x4, which didn’t have a Planar mode, they just added it without replacing an old one, resulting in a total of 10 modes to H.264′s 9. After understanding and writing assembly code for TM pred, I have to say that it is quite a cool idea. Here’s how it works :

  1. Let us take a block of size 4×4, 8×8, or 16×16.

  2. Define the pixels bordering the top of this block (starting from the left) as T[0], T[1], T[2]…

  3. Define the pixels bordering the left of this block (starting from the top) as L[0], L[1], L[2]…

  4. Define the pixel above the top-left of the block as TL.

  5. Predict every pixel <X,Y> in the block to be equal to clip3( T[X] + L[Y] – TL, 0, 255).

  It’s effectively a generalization of gradient prediction to the block level — predict each pixel based on the gradient between its top and left pixels, and the topleft. According to the VP8 devs, it’s chosen by the encoder quite a lot of the time, which isn’t surprising ; it seems like a pretty good idea. As just one more intra pred mode, it’s not going to do magic for compression, but it’s a cool idea and elegantly simple.

  3. Performance and the deblocking filter.
  

  On2 advertised for quite some that VP8′s goal was to be significantly faster to decode than H.264. When I saw the spec, I waited for the punchline, but apparently they were serious. There’s nothing wrong with being of similar speed or a bit slower — but I was rather confused as to the fact that their design didn’t match their stated goal at all. What apparently happened is they had multiple profiles of VP8 — high and low complexity profiles. They marketed the performance of the low complexity ones while touting the quality of the high complexity ones, a tad dishonest. More importantly though, practically nobody is using the low complexity modes, so anyone writing a decoder has to be prepared to handle the high complexity ones, which are the default.

  The primary time-eater here is the deblocking filter. VP8, being an H.264 derivative, has much the same problem as H.264 does in terms of deblocking — it spends an absurd amount of time there. As I write this post, we’re about to finish some of the deblocking filter asm code, but before it’s committed, up to 70% or more of total decoding time is spent in the deblocking filter ! Like H.264, it suffers from the 4×4 transform problem : a 4×4 transform requires a total of 8 length-16 and 8 length-8 loopfilter calls per macroblock, while Theora, with only an 8×8 transform, requires half that.

  This problem is aggravated in VP8 by the fact that the deblocking filter isn’t strength-adaptive ; if even one 4×4 block in a macroblock contains coefficients, every single edge has to be deblocked. Furthermore, the deblocking filter itself is quite complicated ; the “inner edge” filter is a bit more complex than H.264′s and the “macroblock edge” filter is vastly more complicated, having two entirely different codepaths chosen on a per-pixel basis. Of course, in SIMD, this means you have to do both and mask them together at the end.

  There’s nothing wrong with a good-but-slow deblocking filter. But given the amount of deblocking one needs to do in a 4×4-transform-based format, it might have been a better choice to make the filter simpler. It’s pretty difficult to beat H.264 on compression, but it’s certainly not hard to beat it on speed — and yet it seems VP8 missed a perfectly good chance to do so. Another option would have been to pick an 8×8 transform instead of 4×4, reducing the amount of deblocking by a factor of 2.

  And yes, there’s a simple filter available in the low complexity profile, but it doesn’t help if nobody uses it.

  4. Tree-based arithmetic coding.

  Binary arithmetic coding has become the standard entropy coding method for a wide variety of compressed formats, ranging from LZMA to VP6, H.264 and VP8. It’s simple, relatively fast compared to other arithmetic coding schemes, and easy to make adaptive. The problem with this is that you have to come up with a method for converting non-binary symbols into a list of binary symbols, and then choosing what probabilities to use to code each one. Here’s an example from H.264, the sub-partition mode symbol, which is either 8×8, 8×4, 4×8, or 4×4. encode_decision( context, bit ) writes a binary decision (bit) into a numbered context (context).

  8×8 : encode_decision( 21, 0 ) ;

  8×4 : encode_decision( 21, 1 ) ; encode_decision( 22, 0 ) ;

  4×8 : encode_decision( 21, 1 ) ; encode_decision( 22, 1 ) ; encode_decision( 23, 1 ) ;

  4×4 : encode_decision( 21, 1 ) ; encode_decision( 22, 1 ) ; encode_decision( 23, 0 ) ;

  As can be seen, this is clearly like a Huffman tree. Wouldn’t it be nice if we could represent this in the form of an actual tree data structure instead of code ? On2 thought so — they designed a simple system in VP8 that allowed all binarization schemes in the entire format to be represented as simple tree data structures. This greatly reduces the complexity — not speed-wise, but implementation-wise — of the entropy coder. Personally, I quite like it.

  5. The inverse transform ordering.

  I should at some point write a post about common mistakes made in video formats that everyone keeps making. These are not issues that are patent worries or huge issues for compression — just stupid mistakes that are repeatedly made in new video formats, probably because someone just never asked the guy next to him “does this look stupid ?” before sticking it in the spec.

  One common mistake is the problem of transform ordering. Every sane 2D transform is “separable” — that is, it can be done by doing a 1D transform vertically and doing the 1D transform again horizontally (or vice versa). The original iDCT as used in JPEG, H.263, and MPEG-1/2/4 was an “idealized” iDCT — nobody had to use the exact same iDCT, theirs just had to give very close results to a reference implementation. This ended up resulting in a lot of practical problems. It was also slow ; the only way to get an accurate enough iDCT was to do all the intermediate math in 32-bit.

  Practically every modern format, accordingly, has specified an exact iDCT. This includes H.264, VC-1, RV40, Theora, VP8, and many more. Of course, with an exact iDCT comes an exact ordering — while the “real” iDCT can be done in any order, an exact iDCT usually requires an exact order. That is, it specifies horizontal and then vertical, or vertical and then horizontal.

  All of these transforms end up being implemented in SIMD. In SIMD, a vertical transform is generally the only option, so a transpose is added to the process instead of doing a horizontal transform. Accordingly, there are two ways to do it :

  1. Transpose, vertical transform, transpose, vertical transform.

  2. Vertical transform, transpose, vertical transform, transpose.

  These may seem to be equally good, but there’s one catch — if the transpose is done first, it can be completely eliminated by merging it into the coefficient decoding process. On many modern CPUs, particularly x86, transposes are very expensive, so eliminating one of the two gives a pretty significant speed benefit.

  H.264 did it way 1).

  VC-1 did it way 1).

  Theora (inherited from VP3) did it way 1).

  But no. VP8 has to do it way 2), where you can’t eliminate the transpose. Bah. It’s not a huge deal ; probably only 1-2% overall at most speed-wise, but it’s just a needless waste. What really bugs me is that VP3 got it right — why in the world did they screw it up this time around if they got it right beforehand ?

  RV40 is the other modern format I know that made this mistake.

  (NB : You can do transforms without a transpose, but it’s generally not worth it unless the intermediate needs 32-bit math, as in the case of the “real” iDCT.)

  6. Not supporting interlacing.

  THANK YOU THANK YOU THANK YOU THANK YOU THANK YOU THANK YOU THANK YOU.

  Interlacing was the scourge of H.264. It weaseled its way into every nook and cranny of the spec, making every decoder a thousand lines longer. H.264 even included a highly complicated — and effective — dedicated interlaced coding scheme, MBAFF. The mere existence of MBAFF, despite its usefulness for broadcasters and others still stuck in the analog age with their 1080i, 576i , and 480i content, was a blight upon the video format.

  VP8 has once and for all avoided it.

  And if anyone suggests adding interlaced support to the experimental VP8 branch, find a straightjacket and padded cell for them before they cause any real damage.

• create short movie from images and save into mp4 or 3gp format android

  6 juin 2014, par new Project

  I want to make short movie clip from several images. I google very much but got only one answer that to use FFMPEG

  I have tried to compile that native library from ndk-r8 using cygwin in windows 7 but nothing happens and shows compile error.

  I have also try to compile and run github projects github.com/guardianproject/android-ffmpeg and github.com/appunite/AndroidFFmpeg but it also unsucessful.

  when i’ve read projects descriptions then someone told that its only working in linux or mac but i think its not the problem beacause i am using cygwin that used for linux on windows.

  In compiling i’ve got errors like prebuild library not found and something files not found.

  Is there any solution by someone or have compiled library that helps me a lot. Please suggest me some working tutorial or example that saves my lots of days.

  Thanks,.

• 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.