Hybrid Video Coding Based on Bidimensional Matching Pursuit

Hybrid video coding combines together two stages: first, motion estimation and compensation predict each frame from the neighboring frames, then the prediction error is coded, reducing the correlation in the spatial domain. In this work, we focus on the latter stage, presenting a scheme that profits from some of the features introduced by the standard H.264/AVC for motion estimation and replaces the transform in the spatial domain. The prediction error is so coded using the matching pursuit algorithm which decomposes the signal over an appositely designed bidimensional, anisotropic, redundant dictionary. Comparisons are made among the proposed technique, H.264, and a DCT-based coding scheme. Moreover, we introduce fast techniques for atom selection, which exploit the spatial localization of the atoms. An adaptive coding scheme aimed at optimizing the resource allocation is also presented, together with a rate-distortion study for the matching pursuit algorithm. Results show that the proposed scheme outperforms the standard DCT, especially at very low bit rates.

[1]  A. Zakhor Matching Pursuit Video Coding|part Ii: Operational Models for Rate and Distortion , 2002 .

[2]  Pascal Frossard,et al.  High-flexibility scalable image coding , 2003, Visual Communications and Image Processing.

[3]  Avideh Zakhor,et al.  Video compression using matching pursuits , 1999, IEEE Trans. Circuits Syst. Video Technol..

[4]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[5]  Pierre Vandergheynst,et al.  On the exponential convergence of matching pursuits in quasi-incoherent dictionaries , 2006, IEEE Transactions on Information Theory.

[6]  Steven G. Johnson,et al.  FFTW: an adaptive software architecture for the FFT , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[7]  Rémi Gribonval,et al.  Harmonic decomposition of audio signals with matching pursuit , 2003, IEEE Trans. Signal Process..

[8]  Avideh Zakhor,et al.  In-loop atom modulus quantization for matching pursuit and its application to video coding , 2003, IEEE Trans. Image Process..

[9]  Soo-Chang Pei,et al.  SNR scalability based on bitplane coding of matching pursuit atoms at low bit rates: fine-grained and two-layer , 2005 .

[10]  Pierre Vandergheynst,et al.  Very low bit rate image coding using redundant dictionaries , 2003, SPIE Optics + Photonics.

[11]  D. Donoho,et al.  Basis pursuit , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[12]  Wen-Liang Hwang,et al.  SNR scalability based on bitplane coding of matching pursuit atoms at low bit rates: fine-grained and two-layer , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[14]  Pascal Frossard,et al.  New dictionary and fast atom searching method for matching pursuit representation of displaced frame difference , 2002, Proceedings. International Conference on Image Processing.

[15]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[16]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[17]  S. Mallat A wavelet tour of signal processing , 1998 .

[18]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[19]  Antonio Ortega,et al.  Rate-distortion methods for image and video compression , 1998, IEEE Signal Process. Mag..

[20]  Michael Elad,et al.  A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.

[21]  Avideh Zakhor,et al.  Matching pursuit video coding .I. Dictionary approximation , 2002, IEEE Trans. Circuits Syst. Video Technol..

[22]  Avideh Zakhor,et al.  Matching-pursuit video coding .II. Operational models for rate and distortion , 2002, IEEE Trans. Circuits Syst. Video Technol..

[23]  Avideh Zakhor,et al.  Corrections to "matching pursuit video coding-part I: dictionary approximation" , 2002, IEEE Trans. Circuits Syst. Video Technol..

[24]  Henrique S. Malvar,et al.  Low-complexity transform and quantization in H.264/AVC , 2003, IEEE Trans. Circuits Syst. Video Technol..

[25]  Wen-Liang Hwang,et al.  Gain-shape optimized dictionary for matching pursuit video coding , 2003, Signal Process..

[26]  Pascal Frossard,et al.  Efficient image representation by anisotropic refinement in matching pursuit , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[27]  Pascal Frossard,et al.  Redundancy in non-orthogonal transforms , 2001, Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252).

[28]  Morten Nielsen,et al.  Approximation with highly redundant dictionaries , 2003, SPIE Optics + Photonics.