Improving video coding quality by perceptual rate-distortion optimization

The goal of this work is to seek a feasible direction of video coding that can provide a significant quality improvement over H.264. In light of the well-known findings that the distortion metric for video quality has a profound impact on video coding performance and that traditional metrics such as mean square error are poorly correlated with human perception, we identify perceptual video coding, more specifically, perceptual-based rate-distortion optimization, as a sensible approach that has the potential to help drive the performance of video coding to a significantly higher quality level. This technology assessment is supported by experiments with various video sequences, bit-rates, and encoding profiles. The results show that the perceptual-based RDO can indeed bring significant quality improvement for H.264.

[1]  Chung-Lin Huang,et al.  A robust scene-change detection method for video segmentation , 2001, IEEE Trans. Circuits Syst. Video Technol..

[2]  Thomas Wiegand,et al.  Lagrange multiplier selection in hybrid video coder control , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[3]  Chun-Jen Tsai,et al.  Adaptive rate-distortion optimization using perceptual hints , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[4]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[5]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[6]  Lai-Man Po,et al.  A New Rate-Distortion Optimization Using Structural Information in H.264 I-Frame Encoder , 2005, ACIVS.

[7]  Minqiang Jiang,et al.  On enhancing H.264/AVC video rate control by PSNR-based frame complexity estimation , 2005, IEEE Trans. Consumer Electron..

[8]  Chun-Ling Yang,et al.  Gradient-Based Structural Similarity for Image Quality Assessment , 2006, 2006 International Conference on Image Processing.

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

[10]  Lai-Man Po,et al.  A Novel Motion Estimation Method Based on Structural Similarity for H.264 Inter Prediction , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[11]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

[12]  Hua Li,et al.  Perceptually Adaptive Lagrange Multiplier for Rate-Distortion Optimization in H.264 , 2007, Future Generation Communication and Networking (FGCN 2007).

[13]  Xuan Jing,et al.  Improved Frame Level MAD Prediction and Bit Allocation Scheme for H.264/AVC Rate Control , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[14]  Zhibing Wang,et al.  HVS-based structural similarity for image quality assessment , 2008, 2008 9th International Conference on Signal Processing.

[15]  Luís Corte-Real,et al.  H.264 Rate-Distortion Analysis Using Subjective Quality Metric , 2009, FMN.

[16]  Chaofeng Li,et al.  Three-component weighted structural similarity index , 2009, Electronic Imaging.

[17]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[18]  André Kaup,et al.  Laplace Distribution Based Lagrangian Rate Distortion Optimization for Hybrid Video Coding , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[19]  Homer H. Chen,et al.  Perceptual Rate-Distortion Optimization Using Structural Similarity Index as Quality Metric , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[20]  Homer H. Chen,et al.  A perceptual-based approach to bit allocation for H.264 encoder , 2010, Visual Communications and Image Processing.