PeerStreamer

A Quick Description Of Rate Distortion Theory.

We want to encode a video, picture or piece of music optimally. What does

"optimally" really mean? It means that we want to get the best quality at a

given filesize OR we want to get the smallest filesize at a given quality

(in practice, these 2 goals are usually the same).

Solving this directly is not practical; trying all byte sequences 1

megabyte in length and selecting the "best looking" sequence will yield

256^1000000 cases to try.

But first, a word about quality, which is also called distortion.

Distortion can be quantified by almost any quality measurement one chooses.

Commonly, the sum of squared differences is used but more complex methods

that consider psychovisual effects can be used as well. It makes no

difference in this discussion.

First step: that rate distortion factor called lambda...

Let's consider the problem of minimizing:

  distortion + lambda*rate

rate is the filesize

distortion is the quality

lambda is a fixed value choosen as a tradeoff between quality and filesize

Is this equivalent to finding the best quality for a given max

filesize? The answer is yes. For each filesize limit there is some lambda

factor for which minimizing above will get you the best quality (using your

chosen quality measurement) at the desired (or lower) filesize.

Second step: splitting the problem.

Directly splitting the problem of finding the best quality at a given

filesize is hard because we do not know how many bits from the total

filesize should be allocated to each of the subproblems. But the formula

from above:

  distortion + lambda*rate

can be trivially split. Consider:

  (distortion0 + distortion1) + lambda*(rate0 + rate1)

This creates a problem made of 2 independent subproblems. The subproblems

might be 2 16x16 macroblocks in a frame of 32x16 size. To minimize:

  (distortion0 + distortion1) + lambda*(rate0 + rate1)

we just have to minimize:

  distortion0 + lambda*rate0

and

  distortion1 + lambda*rate1

I.e, the 2 problems can be solved independently.

Author: Michael Niedermayer

Copyright: LGPL