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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

• How to cheat on video encoder comparisons

  21 juin 2010, par Dark Shikari — benchmark, H.264, stupidity, test sequences

  Over the past few years, practically everyone and their dog has published some sort of encoder comparison. Sometimes they’re actually intended to be something for the world to rely on, like the old Doom9 comparisons and the MSU comparisons. Other times, they’re just to scratch an itch — someone wants to decide for themselves what is better. And sometimes they’re just there to outright lie in favor of whatever encoder the author likes best. The latter is practically an expected feature on the websites of commercial encoder vendors.

  One thing almost all these comparisons have in common — particularly (but not limited to !) the ones done without consulting experts — is that they are horribly done. They’re usually easy to spot : for example, two videos at totally different bitrates are being compared, or the author complains about one of the videos being “washed out” (i.e. he screwed up his colorspace conversion). Or the results are simply nonsensical. Many of these problems result from the person running the test not “sanity checking” the results to catch mistakes that he made in his test. Others are just outright intentional.

  The result of all these mistakes, both intentional and accidental, is that the results of encoder comparisons tend to be all over the map, to the point of absurdity. For any pair of encoders, it’s practically a given that a comparison exists somewhere that will “prove” any result you want to claim, even if the result would be beyond impossible in any sane situation. This often results in the appearance of a “controversy” even if there isn’t any.

  Keep in mind that every single mistake I mention in this article has actually been done, usually in more than one comparison. And before I offend anyone, keep in mind that when I say “cheating”, I don’t mean to imply that everyone that makes the mistake is doing it intentionally. Especially among amateur comparisons, most of the mistakes are probably honest.

  So, without further ado, we will investigate a wide variety of ways, from the blatant to the subtle, with which you too can cheat on your encoder comparisons.

  Blatant cheating

  1. Screw up your colorspace conversions. A common misconception is that converting from YUV to RGB and back is a simple process where nothing can go wrong. This is quite untrue. There are two primary attributes of YUV : PC range (0-255) vs TV range (16-235) and BT.709 vs BT.601 conversion coefficients. That sums up to a total of 4 possible different types of YUV. When people compare encoders, they often use different frontends, some of which make incorrect assumptions about these attributes.

  Incorrect assumptions are so common that it’s often a matter of luck whether the tool gets it right or not. It doesn’t help that most videos don’t even properly signal which they are to begin with ! Often even the tool that the person running the comparison is using to view the source material gets the conversion wrong.

  Subsampling YUV (aka what everyone uses) adds yet another dimension to the problem : the locations which the chroma data represents (“chroma siting”) isn’t constant. For example, JPEG and MPEG-2 define different positions. This is even worse because almost nobody actually handles this correctly — the best approach is to simply make sure none of your software is doing any conversion. A mistake in chroma siting is what created that infamous PSNR graph showing Theora beating x264, which has been cited for ages since despite the developers themselves retracting it after realizing their mistake.

  Keep in mind that the video encoder is not responsible for colorspace conversion — almost all video encoders operate in the YUV domain (usually subsampled 4:2:0 YUV, aka YV12). Thus any problem in colorspace conversion is usually the fault of the tools used, not the actual encoder.

  How to spot it : “The color is a bit off” or “the contrast of the video is a bit duller”. There were a staggering number of “H.264 vs Theora” encoder comparisons which came out in favor of one or the other solely based on “how well the encoder kept the color” — making the results entirely bogus.

  2. Don’t compare at the same (or nearly the same) bitrate. I saw a VP8 vs x264 comparison the other day that gave VP8 30% more bitrate and then proceeded to demonstrate that it got better PSNR. You would think this is blindingly obvious, but people still make this mistake ! The most common cause of this is assuming that encoders will successfully reach the target bitrate you ask of them — particularly with very broken encoders that don’t. Always check the output filesizes of your encodes.

  How to spot it : The comparison lists perfectly round bitrates for every single test, as opposed to the actual bitrates achieved by the encoders, which will never be exactly matching in any real test.

  3. Use unfair encoding settings. This is a bit of a wide topic : there are many ways to do this. We’ll cover the more blatant ones in this part. Here’s some common ones :

  a. Simply cheat. Intentionally pick awful settings for the encoder you don’t like.

  b. Don’t consider performance. Pick encoding settings without any regard for some particular performance goal. For example, it’s perfectly reasonable to say “use the best settings possible, regardless of speed”. It’s also reasonable to look for a particular encoding speed target. But what isn’t reasonable is to pick extremely fast settings for one encoder and extremely slow settings for another encoder.

  c. Don’t attempt match compatibility options when it’s reasonable to do so. Keyframe interval is a classic one of these : shorter values reduce compression but improve seeking. An easy way to cheat is to simply not set them to the same value, biasing towards whatever encoder has the longer interval. This is most common as an accidental mistake with comparisons involving ffmpeg, where the default keyframe interval is an insanely low 12 frames.

  How to spot it : The comparison doesn’t document its approach regarding choice of encoding settings.

  4. Use ratecontrol methods unfairly. Constant bitrate is not the same as average bitrate — using one instead of the other is a great way to completely ruin a comparison. Another method is to use 1-pass bitrate mode for one encoder and 2-pass or constant quality for another. A good general approach is that, for any given encoder, one should use 2-pass if available and constant quality if not (it may take a few runs to get the bitrate you want, of course).

  Of course, it’s also fine to run a comparison with a particular mode in mind — for example, a comparison targeted at streaming applications might want to test using 1-pass CBR. Of course, in such a case, if CBR is not available in an encoder, you can’t compare to that encoder.

  How to spot it : It’s usually pretty obvious if the encoding settings are given.

  5. Use incredibly old versions of encoders. As it happens, Debian stable is not the best source for the most recent encoding software. Equally, using recent versions known to be buggy.

  6. Don’t distinguish between video formats and the software that encodes them. This is incredibly common : I’ve seen tests that claim to compare “H.264″ against something else while in fact actually comparing “Quicktime” against something else. It’s impossible to compare all H.264 encoders at once, so don’t even try — just call the comparison “Quicktime versus X” instead of “H.264 versus X”. Or better yet, use a good H.264 encoder, like x264 and don’t bother testing awful encoders to begin with.

  Less-obvious cheating

  1. Pick a bitrate that’s way too low. Low bitrate testing is very effective at making differences between encoders obvious, particularly if doing a visual comparison. But past a certain point, it becomes impossible for some encoders to keep up. This is usually an artifact of the video format itself — a scalability limitation. Practically all DCT-based formats have this kind of limitation (wavelets are mostly immune).

  In reality, this is rarely a problem, because one could merely downscale the video to resolve the problem — lower resolutions need fewer bits. But people rarely do this in comparisons (it’s hard to do it fairly), so the best approach is to simply not use absurdly low bitrates. What is “absurdly low” ? That’s a hard question — it ends up being a matter of using one’s best judgement.

  This tends to be less of a problem in larger-scale tests that use many different bitrates.

  How to spot it : At least one of the encoders being compared falls apart completely and utterly in the screenshots.

  Biases towards, a lot : Video formats with completely scalable coding methods (Dirac, Snow, JPEG-2000, SVC).

  Biases towards, a little : Video formats with coding methods that improve scalability, such as arithmetic coding, B-frames, and run-length coding. For example, H.264 and Theora tend to be more scalable than MPEG-4.

  2. Pick a bitrate that’s way too high. This is staggeringly common mistake : pick a bitrate so high that all of the resulting encodes look absolutely perfect. The claim is then made that “there’s no significant difference” between any of the encoders tested. This is surprisingly easy to do inadvertently on sources like Big Buck Bunny, which looks transparent at relatively low bitrates. An equally common but similar mistake is to test at a bitrate that isn’t so high that the videos look perfect, but high enough that they all look very good. The claim is then made that “the difference between these encoders is small”. Well, of course, if you give everything tons of bitrate, the difference between encoders is small.

  How to spot it : You can’t tell which image is the source and which is the encode.

  3. Making invalid comparisons using objective metrics. I explained this earlier in the linked blog post, but in short, if you’re going to measure PSNR, make sure all the encoders are optimized for PSNR. Equally, if you’re going to leave the encoder optimized for visual quality, don’t measure PSNR — post screenshots instead. Same with SSIM or any other objective metric. Furthermore, don’t blindly do metric comparisons — always at least look at the output as a sanity test. Finally, do not claim that PSNR is particularly representative of visual quality, because it isn’t.

  How to spot it : Encoders with psy optimizations, such as x264 or Theora 1.2, do considerably worse than expected in PSNR tests, but look much better in visual comparisons.

  4. Lying with graphs. Using misleading scales on graphs is a great way to make the differences between encoders seem larger or smaller than they actually are. A common mistake is to scale SSIM linearly : in fact, 0.99 is about twice as good as 0.98, not 1% better. One solution for this is to use db to compare SSIM values.

  5. Using lossy screenshots. Posting screenshots as JPEG is a silly, pointless way to worsen an encoder comparison.

  Subtle cheating

  1. Unfairly pick screenshots for comparison. Comparing based on stills is not ideal, but it’s often vastly easier than comparing videos in motion. But it also opens up the door to unfairness. One of the most common mistakes is to pick a frame immediately after (or on) a keyframe for one encoder, but which isn’t for the other encoder. Particularly in the case of encoders that massively boost keyframe quality, this will unfairly bias in favor of the one with the recent keyframe.

  How to spot it : It’s very difficult to tell, if not impossible, unless they provide the video files to inspect.

  2. Cherry-pick source videos. Good source videos are incredibly hard to come by — almost everything is already compressed and what’s left is usually a very poor example of real content. Here’s some common ways to bias unfairly using cherry-picking :

  a. Pick source videos that are already heavily compressed. Pre-compressed source isn’t much of an issue if your target quality level for testing is much lower than that of the source, since any compression artifacts in the source will be a lot smaller than those created by the encoders. But if the source is already very compressed, or you’re testing at a relatively high quality level, this becomes a significant issue.

  Biases towards : Anything that uses a similar transform to the source content. For MPEG-2 source material, this biases towards formats that use the 8x8dct or a very close approximation : MPEG-1/2/4, H.263, and Theora. For H.264 source material, this biases towards formats that use a 4×4 transform : H.264 and VP8.

  b. Pick standard test clips that were not intended for this purpose. There are a wide variety of uncompressed “standard test clips“. Some of these are not intended for general-purpose use, but rather exist to test specific encoder capabilities. For example, Mobile Calendar (“mobcal”) is extremely sharp and low motion, serving to test interpolation capabilities. It will bias incredibly heavily towards whatever encoder uses more B-frames and/or has higher-precision motion compensation. Other test clips are almost completely static, such as the classic “akiyo”. These are also not particularly representative of real content.

  c. Pick very noisy content. Noise is — by definition — not particularly compressible. Both in terms of PSNR and visual quality, a very noisy test clip will tend to reduce the differences between encoders dramatically.

  d. Pick a test clip to exercise a specific encoder feature. I’ve often used short clips from Touhou games to demonstrate the effectiveness of x264′s macroblock-tree algorithm. I’ve sometimes even used it to compare to other encoders as part of such a demonstration. I’ve also used the standard test clip “parkrun” as a demonstration of adaptive quantization. But claiming that either is representative of most real content — and thus can be used as a general determinant of how good encoders are — is of course insane.

  e. Simply encode a bunch of videos and pick the one your favorite encoder does best on.

  3. Preprocessing the source. A encoder test is a test of encoders, not preprocessing. Some encoding apps may add preprocessors to the source, such as noise reduction. This may make the video look better — possibly even better than the source — but it’s not a fair part of comparing the actual encoders.

  4. Screw up decoding. People often forget that in addition to encoding, a test also involves decoding — a step which is equally possible to do wrong. One common error caused by this is in tests of Theora on content whose resolution isn’t divisible by 16. Decoding is often done with ffmpeg — which doesn’t crop the edges properly in some cases. This isn’t really a big deal visually, but in a PSNR comparison, misaligning the entire frame by 4 or 8 pixels is a great way of completely invalidating the results.

  The greatest mistake of all

  Above all, the biggest and most common mistake — and the one that leads to many of the problems mentioned here – is the mistaken belief that one, or even a few tests can really represent all usage fairly. Any comparison has to have some specific goal — to compare something in some particular case, whether it be “maximum offline compression ignoring encoding speed” or “real-time high-speed video streaming” or whatnot. And even then, no comparison can represent all use-cases in that category alone. An encoder comparison can only be honest if it’s aware of its limitations.

• VP8 : a retrospective

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

  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.