Dynamic Computational Complexity and Bit Allocation for Optimizing H.264/AVC Video Compression

In this work we present a novel approach for optimizing H.264/AVC video compression by dynamically allocating computational complexity (such as a number of CPU clocks) and bits for encoding each coding element (basic unit) within a video sequence, according to its predicted MAD (mean absolute difference). Our approach is based on a computational complexity-rate-distortion (C-R-D) analysis, which adds a complexity dimension to the conventional rate-distortion (R-D) analysis. Both theoretically and experimentally, we prove that by implementing the proposed approach better results are achieved. In addition, we present a method and system for implementing the proposed approach, and for controlling computational complexity and bit allocation in real-time and off-line video coding. For allocating a corresponding group of coding modes and the quantization step-size, we develop computational complexity - complexity step - rate (C-I-R) and rate - quantization step-size - computational complexity (R-Q-C) models.