Motion Estimation Optimization for H.264/AVC Using Source Image Edge Features

The H.264/AVC coding standard processes variable block size motion-compensated prediction with multiple reference frames to achieve a pronounced improvement in compression efficiency. Accordingly, the computation of motion estimation increases in proportion to the product of the number of reference frame and the number of intermode. The mathematical analysis in this paper illustrates that the motion-compensated prediction errors are mainly determined by the detailed textures in the source image. The image block being rich in textures contains numerous high-frequency signals, which make variable block size and multiple reference frame techniques essential. On the basis of rate-distortion theory, in this paper, the spatial homogeneity of an image block is made as a relative concept with respect to the current quantization step. For the homogenous block, its futile reference frames and intermodes can be eliminated efficiently. It is further revealed that the sum of absolute differences value of an image block is mainly determined by the sum of its edge gradient amplitude and the current quantization step. Consequently, the image content-based early termination algorithm is proposed, and it outperforms the original method adopted by JVT reference software. Moreover, the dynamic search range algorithm based on the edge gradient amplitude of source image block is analyzed. One eminent advantage of the proposed edge-based algorithms is their efficiency to the macroblock-pipelining architecture, and another desirable feature is their orthogonality to fast block-matching algorithms. Experimental results show that when these algorithms are integrated with hybrid unsymmetrical-cross multi-hexagongrid search, an averaged 31.4-60.0% motion estimation time can be saved, whereas the averaging BDPSNR loss is 0.0497 dB for all tested sequences.