Ofer Hadar

Recent developments in computer and communication technologies have stimulated a great interest in digital technologies for compressing and transmitting video information. There are many well-established methods for spatial and temporal prediction, entropy coding and quantization. On one hand, several methods in the H.264/AVC video coding are independently effective, but they do not solve common video coding problems optimally, since they provide the optimal solution for each of the video compression parts independently and usually do not utilize the two main constraints of the overall video encoding task, namely transmitted bit-rate and computational load that can drastically vary in demanding modern communication environments. On the other hand, other methods present techniques that provide high quality video coding with a low computational load, utilizing variationsin transmission channel conditions. In this chapter, four major video coding optimization issues have been presented in detail: rate control optimization, computational complexity control optimization, joint computational complexity and rate control optimization, and transform coding optimization. These optimization methods are especially useful for future Internet and 4G applications with limited computational resources, such as video conferencing (between two or more mobile users), video transrating, video transcoding between MPEG-2 and H.264/AVC videocoding standards, and the like. The presented approaches, such as the computational complexity and bit allocation for optimizing H.264/AVC video compression can be integrated to develop an efficient optimized video encoder, which will enable: (i) selecting computational load and transmitted bit rate, (ii) selecting quantization parameters, (iii) selecting coding modes, (iv) selecting motion estimation algorithm for each type of an input video signal, and (v) selecting appropriate transform coding.