Time-efficient learning theoretic algorithms for H.264 mode selection

Abstract

The H.264 video coding standard derives much of its compression efficiency gain from the use of multiple different macroblock prediction modes for macroblock coding. In general, finding the prediction mode which gives optimal R-D performance for a given macroblock requires the encoder to completely encode the macroblock using all possible prediction modes. This results in a significant increase in encoder computational complexity. In this paper, we present a mode selection framework for H.264 which uses learning theoretic classification algorithms to discern between broad mode classes, based on the evaluation of a simple set of macroblock features. We show that the proposed mode selection framework significantly reduces encoder computational complexity, at the cost of only a small loss in compression performance. © 2004 IEEE.