Why Modern Video Codecs Suck and Will Keep on Sucking

If you look at the modern video codecs you’ll spot one problem: they get designed for large resolutions and follow one-size-does-not-fit-exactly-anybody approach. By that I mean that codecs are following the model introduced by ITU H.261—split image into blocks, predict block from the previous frame if possible, apply DCT, quantise and code resulting coefficients (using zigzag scan order and special treatment for runs of zeroes). The same was later applied to pictures in JPEG format that is still staying strong.

Of course modern codecs are much more complex that that, current ITU H.EVC standard enhanced every stage:

  • image is no longer split into 8×8 blocks, you have quadtrees coding blocks from 64×64 down to 4×4 pixels;
  • block prediction got more complicated, now you have intra (or spatial prediction) that tries to fill block with gradient derived from already decoded neighbour blocks) and inter prediction (the old prediction from the previous frame);
  • and obviously inter prediction is not that simple either: now it’s decoupled from transformed block and can have completely different sizes (like 16×4 or 24×32), instead of single previous frame you can use two reference frames selected from two separate lists of references and even motion vectors are often predicted using motion vectors from the reference frames (does anybody like implementing those colocated MV prediction modes BTW?);
  • DCT is replaced with some bitexact integer approximations (and the dequantisation and/or transform stages may be skipped completely);
  • there are more scan types used and all values are coded using some context-adaptive coder.

Plus some hacks for low-resolution mode (e.g. special 4×4 transform for luma), lossless (or as they call it, “PCM coding”) and now also special coding mode for screen content (i.e. images with fewer distinct colours and where fine details matter).

The enhancements on streamline coding process are enhancements, they don’t change principles of coding but rather adapt them to modern conditions (meaning that there’s demand in higher compression and there’s more CPU power and RAM can be thrown at the processing—mostly RAM though).

And what the hacks do? They try to deal with the fact that this model works fine for smooth changing continuous tone images and it does not work that good on other types of video source. There are several ways to deal with the problem but keep in mind that the problem of distinguishing video types and selecting proper coding is AI-complete:

  1. JPEG+PNG approach. You select best coder for the source manually and transmit it like that. Obviously it works well in limited scenarios but even people quite often don’t bother and compress everything with the single format even if that hurts quality or compression ratio. Plus you need to handle two different formats, make sure that the receiving end also supports them etc etc.
  2. MPEG-4 approach. You have single format that has various “coding tools” embedded, they can be both full alternative coding features (like WebP has VP8 compression and lossless compression and nothing common between them or MPEG-4 Audio can be coded as conventional AAC, TwinVQ, speech codec or even as a description for synthesised audio) or various enhancement applied to the main coding method (like you have AAC-LC, AAC-Main that enables several features or HE-AACv2 which takes AAC-LC audio and applies SBR and Parametric Stereo to double its channels and frequency range). Actually there are more than forty various MPEG-4 Audio object types (various coding modes) already, do you think there’s any software that supports everything? And looks like modern video codecs head this way too: they introduce various coding tools (like for screen content) and it would be fun to support all possible features in the decoder. Please consider how much effort should be spent on effectively applying all those tools too (and that’s obviously beside the scope of standards).
  3. ZPAQ approach. The terminal AI-complete solution. You are not merely generating bitstream but first you need to transmit bytecode for a program that will decode this bytestream. It’s the ultimate solution—if you can describe the perfect model for the stream then you can compress it the best. Finding an optimal model for given bitstream is left as an exercise for the reader (in TAoCP it would be marked with M60 I guess).

The second thing I find sucky is combinatorial explosion of encoding parameters. Back in the day you had to worry about selecting the best quantisation matrix (or merely a quantiser) and motion vector if you decided to code it as inter-block. Now you have countless ways to split large tile into smaller blocks, many ways to select prediction mode (inter/intra, prediction angle for intra, partitioning, reference frames and motion vectors) and whether to skip transform stage or not and if not whether it’s worth to subdivide block further or not… The result is as good as string theory—you can get a good one if you can guess zillions of parameters right.

It would be nice to have encoder actually splitting video into scene and actors and transmitting just the changes to the objects (actors, scene) instead of blocks. But then you have a problem of coding those descriptions efficiently and even greater problem of automatically classifying the video into such objects (obviously software can do that, that’s why MPEG-4 Synthetic Video is such a great success). Actually it had some use: there was AVS-S standard for coding video specifically from surveillance cameras (why would China need such standard anyway?). In this standard there was special kind of frame for the whole scene and the main share of video was supposed to be just objects moving around the scene. Even if the standard is obsolete its legacy was included into HEVSAVS2 as three or four new special frame types.

Personally I believe that current video formats are being optimised to local minimum, there are probably other coding methods that give larger gain on certain kinds of data, preferably with less tweaking. For example, that was probably the best thing about Daala, its PVQ coding; the rest was nor crazy enough. I have a gut feeling that vector quantisation might be a good base for an alternative approach to building video codecs. And I think it’s better to have different formats oriented for e.g. low-latency broadcasting and video distributing. If you remember, back in the days people actually spent time to decide which segment was coded better with DivX ;-) 3 Fast-Motion or DivX ;-) 3 Low-Motion, so those who care will be able to select proper format. And the rest can keep watching content in VP11/AV2 format. Probably only the last sentence will come to life.

That’s why I don’t expect bright future in video codecs and that’s why my blog is titled like this.

4 Responses to “Why Modern Video Codecs Suck and Will Keep on Sucking”

  1. Luca Barbato says:

    The idea of mixing and matching “tools” in a bitstream makes me think one could move mix and matching at container level to make everything even more confusing.

    I wonder when it will appear in mkv.

  2. Kostya says:

    You forget about the already existing tools for shaving off bits in frame data (ContentCompAlgo=3). The framework is mostly there, it just needs to be extended a bit.

  3. ttoinou says:

    “ZPAQ approach” looks like what some do with new images format on the internet, throwing the JS decoder (soon webassembly things), and what could be done if someone invents a new video format that can be decoded in GLSL shaders 🙂

    In think we will soon discover new way of compressing very efficiently “real world” content (people, building etc.. and not psychedelic / VFX stuff) thanks to recent discoveries in neural networks.

    We might even train neural networks to find back the source content (before compression) with high quality, and be able to restore old bad compressed videos

  4. Kostya says:

    It might be so indeed, though I’m not very optimistic about neural networks. And keep in mind you’ll need to store all neural network coefficients if you want offline decompression and they may easily take more space than the source video.

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