Efficient mode selection for H.264 complexity reduction in a Bayesian framework

In order to achieve a high compression ratio, the H.264/AVC standard has incorporated a large number of coding modes which must be evaluated during the coding process to determine the optimal rate-distortion tradeoff. The coding gains of H.264/AVC arise at the expense of significant coder complexity which may not be desired for mobile devices with limited battery life. One coder process that has been identified as having potential for achieving computation savings is the selection between skipping the coding of a macroblock and coding of the macroblock in one of the remaining coding modes. In low-motion subsequences, a large percentage of macroblocks are “skipped”, that is, no coded data are transmitted for these macroblocks. By estimating which macroblocks are to be skipped during the coding process, significant savings in computation can be realized, since the coder then does not evaluate the rate-distortion costs of all candidate coding modes. In this work, we place this skip versus code decision in a Bayesian framework. We use the rate-distortion cost difference between coding and skipping a macroblock as the single decision feature and determine an appropriate decision threshold following modeling of the cost difference's class-conditional PDFs. Finally, in order to further limit system complexity, we model the threshold's parameters as functions of application- and sequence-specific characteristics, namely, the quantization parameter and an activity factor. This results in a decision threshold that is only a function of these two characteristics, which are either known or easily measured. It is shown that this approach can result in a time savings of over 80% for low-motion sequences at a negligible decrease or, in certain cases, a slight increase in quality over a reference H.264 codec.