On Optimizing H. 264/AVC Rate Control by Improving R-D Model and Incorporating HVS Characteristics

The state-of-the-art JVT-G012 rate control algorithm of H.264 is improved from two aspects. First, the quadratic rate-distortion (R-D) model is modified based on both empirical observations and theoretical analysis. Second, based on the existing physiological and psychological research findings of human vision, the rate control algorithm is optimized by incorporating the main characteristics of the human visual system (HVS) such as contrast sensitivity, multichannel theory, and masking effect. Experiments are conducted, and experimental results show that the improved algorithm can simultaneously enhance the overall subjective visual quality and improve the rate control precision effectively.

[1]  King Ngi Ngan,et al.  Recent advances in rate control for video coding , 2007, Signal Process. Image Commun..

[2]  Zhu Zhong.  New Algorithm for Extracting Moving Object Based on Spatio-temporal Information , 2003 .

[3]  Nam Ling,et al.  Improved H.264 rate control by enhanced MAD-based frame complexity prediction , 2006, J. Vis. Commun. Image Represent..

[4]  Alan C. Bovik,et al.  41 OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[5]  A. Bovik,et al.  OBJECTIVE VIDEO QUALITY ASSESSMENT , 2003 .

[6]  Zhen Liu,et al.  JPEG2000 encoding with perceptual distortion control , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  Tihao Chiang,et al.  A new rate control scheme using quadratic rate distortion model , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[8]  Rahul Vanam,et al.  Improved Rate Control and Motion Estimation for H.264 Encoder , 2007, 2007 IEEE International Conference on Image Processing.

[9]  Chun-Jen Tsai,et al.  Visual sensitivity guided bit allocation for video coding , 2006, IEEE Transactions on Multimedia.

[10]  Yo-Sung Ho,et al.  A Frame-Layer Rate Control Algorithm for H.264 Using Rate-Dependent Mode Selection , 2005, PCM.

[11]  Ravi Sankar,et al.  An algorithm for image quality assessment , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  Neil W. Bergmann,et al.  A technique for image quality assessment based on a human visual system model , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[13]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[14]  Jenq-Neng Hwang,et al.  A novel hybrid HVPC/mathematical model rate control for low bit-rate streaming video , 2002, Signal Process. Image Commun..

[15]  Marios S. Pattichis,et al.  Foveated video compression with optimal rate control , 2001, IEEE Trans. Image Process..

[16]  Wen-Huang Cheng,et al.  Automatic video region-of-interest determination based on user attention model , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[17]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[18]  Yücel Altunbasak,et al.  Frame bit allocation for the H.264/AVC video coder via Cauchy-density-based rate and distortion models , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Minqiang Jiang,et al.  On Lagrange multiplier and quantizer adjustment for H.264 frame-layer video rate control , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[21]  Alan C. Bovik,et al.  Image information and visual quality , 2006, IEEE Trans. Image Process..