TY - CHAP
T1 - Optimization Methods for H.264/AVC Video Coding
AU - Grois, Dan
AU - Kaminsky, Evgeny
AU - Hadar, Ofer
N1 - Funding Information:
Acknowledgments. The authors wish to express their sincere appreciation to all of the Japanese staff for their installation and operation of the Hurghada seismic network. The project was funded by Japan International Cooperation Agency (JICA), and carried out with the cooperation of Japan and Egypt (National Research Institute of Astronomy and Geophysics, NRIAG). We wish to thank all the Egyptian groups for their operation and providing us with data. We gratefully thank Prof. E. Kissling for his helpful advice. We also appreciate the comments of two anonymous referees, which greatly improved the manuscript.
PY - 2010/10/4
Y1 - 2010/10/4
N2 - 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.
AB - 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.
KW - Bit allocation
KW - Coding modes
KW - Complexity allocation
KW - Complexity-rate-distortion (C-R-D)
KW - Computational complexity
KW - Diamond search (DS)
KW - Dynamic allocation
KW - Fast motion estimation (FME)
KW - Lagrangian Optimization
KW - Motion estimation (ME)
KW - Rate Distortion Optimization (RDO)
KW - Rate control
KW - Rate-Distortion Theory
UR - http://www.scopus.com/inward/record.url?scp=80155140265&partnerID=8YFLogxK
U2 - 10.1002/9780470974582.ch7
DO - 10.1002/9780470974582.ch7
M3 - Chapter
AN - SCOPUS:80155140265
SN - 9780470750070
SP - 175
EP - 204
BT - The Handbook of MPEG Applications
PB - John Wiley and Sons
ER -