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• Studying A Game Wave Disc

  23 novembre 2010, par Multimedia Mike — Game Hacking

  I picked up a used copy of game called Gemz — a rather flagrant Bejeweled clone — for a game console called Game Wave Family Entertainment System. Heard of it ? Neither had I. But the game media is optical, so I had to get it and study it.

  
  
  

  When mounted in Linux (as UDF), the disc is reported to contain 2.8 GB of data, so it has to be a DVD. 810 MB of that is dedicated to the movies/ directory. Multimedia format ? Just plain, boring MPEG files (very YouTube-friendly— here’s the opening animation). Deeper digging reveals some more subdirectories called movies/ that, combined, occupy the lion’s share of the disc space. Additionally, there are several single-frame .m2v files in a directory called iframes/ which are used to encode things like load screens.

  
  
  

  There are more interesting data files including .zbm files for images and fonts, and .zwf files for audio. I suspect that these stand for zipped bitmap and zipped wave file, respectively. They can’t be directly unzipped with ’gunzip’. Some of the numbers at the start of some files lead me to believe they can be easily decompressed with standard zlib facilities.

  Based on the binary files on the Gemz disc, I couldn’t find any data on what CPU this system might use. A little Googling led me to this page at the Video Game Console Library which pegs the brain as a Mediamatics 6811. Some searching for that leads me to a long-discontinued line of hardware from National Semiconductor.

  The Console Library page also mentions that the games were developed using the Lua programming language. Indeed, there are many Lua-related strings in the game’s binaries (’zlib’ also makes an appearance).

• Minimal Understanding of VP8′s Forward Transform

  16 novembre 2010, par Multimedia Mike — VP8

  Regarding my toy VP8 encoder, Pengvado mentioned in the comments of my last post, “x264 looks perfect using only i16x16 DC mode. You must be doing something wrong in computing residual or fdct or quantization.” This makes a lot of sense. The encoder generates a series of elements which describe how to reconstruct the original image. Intra block reconstruction takes into consideration the following elements :

  
  
  

  I have already verified that both my encoder and FFmpeg’s VP8 decoder agree precisely on how to reconstruct blocks based on the predictors, coefficients, and quantizers. Thus, if the decoded image still looks crazy, the elements the encoder is generating to describe the image must be wrong.

  So I started studying the forward DCT, which I had cribbed wholesale from the original libvpx 0.9.0 source code. It should be noted that the formal VP8 spec only defines the inverse transform process, not the forward process. I was using a version designated as the “short” version, vs. the “fast” version. Then I looked at the 0.9.5 FDCT. Then I got the idea of comparing the results of each.

  input:   92 91 89 86 91 90 88 86 89 89 89 88 89 87 88 93

  • libvpx 0.9.0 “short” :

    forward : -314 5 1 5 4 5 -2 0 0 1 -1 -1 1 11 -3 -4
    inverse : 92 91 89 86 89 86 91 90 91 90 88 86 88 86 89 89
    

  • libvpx 0.9.0 “fast” :

    forward : -314 4 0 5 4 4 -2 0 0 1 0 -1 1 11 -2 -5
    inverse : 91 91 89 86 88 86 91 90 91 90 88 86 88 86 89 89
    

  • libvpx 0.9.5 “short” :

    forward : -312 7 1 0 1 12 -5 2 2 -3 3 -1 1 0 -2 1
    inverse : 92 91 89 86 91 90 88 86 89 89 89 88 89 87 88 93
    

  I was surprised when I noticed that input[] != idct(fdct(input[])) in some of the above cases. Then I remembered that the aforementioned property isn’t what is meant by a “bit-exact” transform– only that all implementations of the inverse transform are supposed to produce bit-exact output for a given vector of input coefficients.

  Anyway, I tried applying each of these forward transforms. I got slightly differing results, with the latest one I tried (the fdct from libvpx 0.9.5) producing the best results (to my eye). At least the trees look better in the Big Buck Bunny logo image :

  
  
  The dense trees of the Big Buck Bunny logo using one of the libvpx 0.9.0 forward transforms
  
  
  The same segment of the image using the libvpx 0.9.5 forward transform
  

  Then again, it could be that the different numbers generated by the newer forward transform triggered different prediction modes to be chosen. Overall, adapting the newer FDCT did not dramatically improve the encoding quality.

  Working on the intra 4×4 mode encoding is generating some rather more accurate blocks than my intra 16×16 encoder. Pengvado indicated that x264 generates perfectly legible results when forcing the encoder to only use intra 16×16 mode. To be honest, I’m having trouble understanding how that can possibly occur thanks to the Walsh-Hadamard transform (WHT). I think that’s where a lot of the error is creeping in with my intra 16×16 encoder. Then again, FFmpeg implements an inverse WHT function that bears ‘vp8′ in its name. This implies that it’s custom to the algorithm and not exactly shared with H.264.

• Announcing the World’s Worst VP8 Encoder

  5 octobre 2010, par Multimedia Mike — Outlandish Brainstorms, VP8

  I wanted to see if I could write an extremely basic VP8 encoder. It turned out to be one of the hardest endeavors I have ever attempted (and arguably one of the least successful).

  Results
  I started with the Big Buck Bunny title image :

  
  
  

  And this is the best encoding that this experiment could yield :

  
  
  

  Squint hard enough and you can totally make out the logo. Pretty silly effort, I know. It should also be noted that the resultant .webm file holding that single 400×225 image was 191324 bytes. When FFmpeg decoded it to a PNG, it was only 187200 bytes.

  The Story
  Remember my post about a naive SVQ1 encoder ? Long story short, I set out to do the same thing with VP8. (I wanted to the same thing with VP3/Theora for years. But take a good look at what it would entail to create even the most basic bitstream. As involved as VP8 may be, its bitstream is absolutely trivial compared to VP3/Theora.)
  

  With the naive SVQ1 encoder, the goal was to create a minimally compliant SVQ1 encoded bitstream. For this exercise, I similarly hypothesized what it would take to create the most basic, syntactically correct VP8 bitstream with the least amount of effort. These are the overall steps I came up with :

  • Intra-only
  • Create a basic bitstream header that disables any extra features (no modification of default tables)
  • Use a static quantizer
  • Use intra 16×16 coding for each macroblock
  • Use vertical prediction for the 16×16 intra coding

  For coding each macroblock :

  • Subtract vertical predictor from each row
  • Perform forward transform on each 4×4 sub block
  • Perform forward WHT on luma plane DCT coefficients
  • Pack the coefficients into the bitstream via the Boolean encoder

  It all sounds so simple. But, like I said in the SVQ1 post, it’s all very much like carefully bootstrapping a program to run on a particular CPU, and the VP8 decoder serves as the CPU. I’m confident that I have the bitstream encoding correct because, at the very least, the decoder agrees precisely with the encoder about the numbers represented by those 0s and 1s.

  What’s Wrong ?
  Compromises were made for the sake of getting some vaguely recognizable image encoded in a minimally valid manner. One big stumbling block is that I couldn’t seem to encode an end of block (EOB) condition correctly. I then realized that it’s perfectly valid to just encode a lot of zero coefficients rather than signaling EOB. An encoding travesty, I know, and likely one reason that the resulting filesize is so huge.

  More drama occurred when I hit my first block that had all zeros. There were complications in that situation that I couldn’t seem to avoid. So I forced the first AC coefficient to be 1 in that case. Hey, the decoder liked it.

  As for the generally weird look of the decoded image, I’m thinking that could either be : A) an artifact of forcing 16×16 vertical prediction or ; or B) a mistake in the way that I transformed and predicted stuff before sending it to the decoder. The smart money is on a combination of both A and B.

  Then again, as the SVQ1 experiment demonstrated, I shouldn’t expect extraordinary visual quality when setting the bar this low (i.e., just getting some bag of bits that doesn’t make the decoder barf).