12/17/2014

12-17-14 - PVQ Vector Distribution Note

So, PVQ, as in "Pyramid VQ" is just a way of making a VQ codebook for a unit vector that has a certain probability model (each value is laplacian (the same laplacian) and independent).

You have a bunch of values, you send the length separately so you're left with a unit vector. Independent values are not equally distributed on the unit sphere, so you don't want a normal unit vector quantizer, you want this one that is scaled squares.

Okay, that's all fine, but the application to images is problematic.

In images, we have these various AC residuals. Let's assume 8x8 blocks for now for clarity. In each block, you have ACij (ij in 0-7 and AC00 excluded). The ij are frequency coordinates for each coefficient. You also have spatial neighbors in the adjacent blocks.

The problem is that the AC's are not independent - their not independent either in frequency or spatial coordinates. AC42 is strongly correlated to AC21 and also to AC42 in the neighboring blocks. They also don't have the same distribution; lower freqency-index AC's have much higher means.

In order to use Pyramid VQ, we need to find a grouping of AC's into a vector, such that the values we are putting in that vector are as uncorrelated and equally distributed as possible.

One paper I found that I linked in the previous email forms a vector by taking all the coefficients in the same frequency slot in a spatial region (for each ij, take all ACij in the 4x4 neighorhood of blocks). This is appealing in the sense that it gathers AC's of the same frequency subband, so they have roughly the same distribution. The problem is there are strong spatial correlations.

In Daala they form "subbands" of the coefficients by grouping together chunks of ACs that are in similar frequency groups.

The reason why correlation is bad is that it makes the PVQ codebook not optimal. For correlated values you should have more codebook vectors with neighboring values similar. eg. more entries around {0, 2, 2, 0} and fewer around {2, 0, 0, 2}. The PVQ codebok assumes those are equally likely.

You can however, make up for this a bit with the way you encode the codebook index. It doesn't fix the quantizer, but it does extract the correlation.

In classical PVQ (P = Pyramid) you would simply form an index to the vector and send it with an equiprobable code. But in practice you might do it with binary subdivision or incremental enumeration schemes, and then you need not make all codebook vectors equiprobable.

For example in Daala one of the issues for the lower subbands is that the vectors that have signal in the low AC's are more probable than the high AC's. eg. for subband that spans AC10 - AC40 , {1,0,0,0} is much more likely than {0,0,0,1}.

Of course this becomes a big mess when you consider Predictive VQ, because the Householder transform scrambles everywhere up in a way that makes it hard to model these built-in skews. On the other hand, if the Predictive VQ removes enough of the correlation with neighbors and subband, then you are left with a roughly evenly distributed vector again which is what you want.

1 comment:

Tim Terriberry said...

"For correlated values you should have more codebook vectors with neighboring values similar. eg. more entries around {0, 2, 2, 0} and fewer around {2, 0, 0, 2}. The PVQ codebok assumes those are equally likely."

You could apply the same argument to scalar quantization. You make up for it in the probability modeling in both cases (though what we're currently doing there could certainly be improved).

One of the things that takes a bit to wrap your head around is that we're not using VQ here for any of the traditional "memory advantage", etc., reasons people normally use VQ. We want to do gain-shape quantization entirely for perceptual reasons, and we use VQ for the shape almost entirely to avoid adding an extra (redundant) degree of freedom.

old rants