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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

• Creating A Lossless SMC Encoder

  26 avril 2011, par Multimedia Mike — General

  Look, I can’t explain how or why I come up with this stuff. For some reason, I thought it would be interesting to write a new encoder for the Apple SMC video codec. I can’t even remember why. I just sat down the other day, started writing, and now I have a lossless SMC encoder that I’m not sure what to do with. Maybe this is to be my new thing— writing encoders for marginal multimedia formats.

  Introduction
  SMC is a vector quantizer (a lossy method) but I decided to attack it from the angle of lossless encoding. A.k.a. Apple Graphics Codec, SMC operates on 4x4 blocks in an 8-bit paletted colorspace. Each 4x4 block can be encoded with 1, 2, 4, 8, or 16 colors. Blocks can also be skipped (copied from previous frame) or copied from blocks rendered immediately prior within the same frame.

  Step 1 : Validating Infrastructure
  The goal of this step is to encode the most braindead SMC frame possible and see if FFmpeg/libav’s QuickTime muxer can create a valid file. I think the simplest frame would be one in which each vector is encoded with the single-color mode, starting with color 0 and incrementing through the palette.

  Status : Successful. The only ’trick’ was to set avctx->bits_per_coded_sample to 8. (For fun, this can also be set to 40 (8 | 0x20) to specify a grayscale palette.)
  

  
  
  

  Step 2 : Preprocessing
  The video frames will arrive at the encoder as 32-bit RGB. These will need to be converted to a paletted colorspace before encoding. I don’t want to use FFmpeg’s default dithering approach as this will result in a substantial loss of quality as described in this post. I would rather maintain a palette built from observed colors throughout successive frames. If the total number of unique observed colors ever exceeds 256, error out.

  That’s what I would like to do. However, I noticed that FFmpeg/libav’s QuickTime muxer has never taken into account the possibility of encoding palettes. The path of least resistance in this case is to dither the input to match QuickTime’s default 8-bit palette (if a paletted QuickTime file does not specify a palette, a default 1-, 2-, 4-, or 8-bit palette is selected).

  Status : Successful, if slow. I definitely need to optimize this step later.

  Step 3 : Most Naive Encoding
  The most basic encoding is to "encode" each block as a 16-color block. This will actually result in a slightly larger frame size than a raw encoding since each 4x4 block will be prepended by a byte opcode (0xE0 in this case) to indicate encoding mode. This should demonstrate that the encoder is functioning at the most basic level.

  Status : Successful. Try not to laugh too hard at the Big Buck Bunny dithered to an 8-bit palette :

  
  
  

  Step 4 : Better Representation
  It seems to me that encoding this format (losslessly) will entail performing vector operations on lots of 16-element (4x4-pixel) vectors. These could be done on the frame as-is, but it strikes me as more efficient and perhaps less error prone to rearrange the input images into a vector of vectors (or array of arrays if you prefer) :

    0  1  2  3  w ...
    4  5  6  7  x ...
    8  9  A  B  y ...
    C  D  E  F  z ...
  

    0 : [0 1 2 3 4 5 6 7 8 9 A B C D E F]
    1 : [...]
  

  Status : Successful.

  Step 5 : Add Interframe Skip Codes
  Time to add a bit of brainpower to the proceedings : On non-keyframes, compare the current vector to the vector at the same position from the previous frame.

  Test this by encoding a pair of identical frames. Ideally, all codes should be skip codes.

  Status : Successful, though my vector matching function could probably be improved.

  Step 6 : Analyze Blocks For Optimal Color Coding
  This is where things get potentially interesting, algorithmically. At least, I need to figure out (or look up) an algorithm to count the unique elements in a vector.

  Naive algorithm (i.e., first thing I can think of) :

  • initialize a count variable to 0
  • initialize an array of 256 flags to false
  • for each 8-bit element in vector :
    • if flag array[element] is 0, set array[element] to true and increment count

  Status : Successful. Here is the distribution for the 640x360 Big Buck Bunny title :

  1194 4636 4113 2140 1138 568 325 154 80 36 9 5 2 0 0 0

  Or, in pretty graph form, demonstrating that vectors with few distinct elements dominate :
  

  
  
  

  Step 7 : Encode Monochrome Blocks
  At this point, the structure is starting to come together pretty well. This phase involves encoding a 0x60 opcode and a palette index when the count_distinct() function returns 1.

  Status : Absolutely no problem.

  Step 8 : Encode 2-, 4-, and 8-color Modes
  This step is a little more involved. This is where SMC’s 2-, 4-, and 8-color circular palette caches come into play. E.g., when the first 2-color block is encoded, the pair of colors it uses will be inserted into entry 0 of the 2-color cache. During the next 2-color block encoding, if the block uses a pair of colors that already occurs in the cache, the encoding can reference that cache entry. Otherwise, it adds the pair to the next available cache entry, looping back around to 0 as necessary.

  I think I should modify the count_distinct() function to also return a 16-byte array that contains a sorted list of the palette indicies used in the vector. The color pair cache will contain 256 16-bit, 32-bit ints for the quads and 64-bit ints for the octets. This will allow a slightly faster linear cache search.

  Status : The 2-color encoding wasn’t too much trouble and I was able to adapt it to the 4-color mode pretty quickly afterward. I’m still having trouble with the insane 8-color coding mode, though. So that’s commented out for the time being.

  Step 9 : Run Encoding and Putting It All Together
  For each frame, convert the input pixels to a paletted format via one method or another (match to default QuickTime palette for first pass). Then, preprocess each vector to determine the minimum number of elements that can be used to represent it, storing the sorted list of distinct colors in a separate array. The number of elements can either be 0 (only for interframes and indicates a skip block), 1, 2, 4, 8, or 16. Also during this phase, for each vector after the first, test if the vector is the same as the previous vector. If it is, denote this fact in the preprocessed encoding (set the high bit of the element count number).

  Finally, pack it into the bytestream. Iterate through the element count array and search for the longest runs of elements that are encoded with the same mode (up to 256 for skip modes, up to 16 for other modes). If the high bit of an element count is set, that indicates that a copy mode can be encoded. Look for the longest run of element counts with the high bit set and encode a copy mode.

  Status : In-process. Will finish this as motivation strikes.

• Further SMC Encoding Work

  25 août 2011, par Multimedia Mike — General

  Sometimes, when I don’t feel like doing anything else, I look at that Apple SMC video encoder again.

  8-bit Encoding
  When I last worked on the encoder, I couldn’t get the 8-color mode working correctly, even though the similar 2- and 4-color modes were working fine. I chalked the problem up to the extreme weirdness in the packing method unique to the 8-color mode. Remarkably, I had that logic correct the first time around. The real problem turned out to be with the 8-color cache and it was due to the vagaries of 64-bit math in C. Bit shifting an unsigned 8-bit quantity implicitly results in a signed 32-bit quantity, or so I discovered.

  Anyway, the 8-color encoding works correctly, thus shaving a few more bytes off the encoding size.

  Encoding Scheme Oddities
  The next step is to encode runs of data. This is where I noticed some algorithmic oddities in the scheme that I never really noticed before. There are 1-, 2-, 4-, 8-, and 16-color modes. Each mode allows encoding from 1-256 blocks of that same encoding. For example, the byte sequence :

    0x62 0x45
  

  Specifies that the next 3 4×4 blocks are encoded with single-color mode (of byte 0×62, high nibble is encoding mode and low nibble is count-1 blocks) and the palette color to be used is 0×45. Further, opcode 0×70 is the same except the following byte allows for specifying more than 16 (i.e., up to 256) blocks shall be encoded in the same matter. In light of this repeat functionality being built into the rendering opcodes, I’m puzzled by the existence of the repeat block opcodes. There are opcodes to repeat the prior block up to 256 times, and there are opcodes to repeat the prior pair of blocks up to 256 times.

  So my quandary is : What would the repeat opcodes be used for ? I hacked the FFmpeg / Libav SMC decoder to output a histogram of which opcodes are used. The repeat pair opcodes are never seen. However, the single-repeat opcodes are used a few times.

  Puzzle Solved ?
  I’m glad I wrote this post. Just as I was about to hit “Publish”, I think I figured it out. I haven’t mentioned the skip opcodes yet– there are opcodes that specify that 1-256 4×4 blocks are unchanged from the previous frame. Conceivably, a block could be unchanged from the previous frame and then repeated 1-256 times from there.

  That’s something I hadn’t thought of up to this point for my proposed algorithm and will require a little more work.

  Further reading

  • My first entry regarding my toy SMC encoder