Reconstruction for Distributed Video Coding: A Context-Adaptive Markov Random Field Approach

• Published in: IEEE transactions on circuits and systems for video technology Vol. 21; no. 8; pp. 1100 - 1114
• Main Authors: Zhang, Yongsheng, Xiong, Hongkai, He, Zhihai, Yu, Songyu, Chen, Chang Wen
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Coding; Confidence intervals; Correlation; Decoding; Detection, estimation, filtering, equalization, prediction; Distributed video coding (DVC); Encoding; Estimates; Exact sciences and technology; Image processing; Image reconstruction; Information, signal and communications theory; Magnetorheological fluids; Markov random fields (MRFs); Mathematical models; maximum a posteriori; Mean square errors; Pixel; Pixels; Quantization; Reconstruction; Reconstruction algorithms; Signal and communications theory; Signal processing; Signal, noise; Statistical correlation; Studies; Telecommunications and information theory; Wyner-Ziv coding; Video coding; Performance evaluation; Energy function; Distributed video coding (DVC); Artefact; Modeling; Video signal processing; Mean square error; Random field; Wyner―Ziv coding; A posteriori estimation; Probabilistic approach; Motion estimation; Critical parameter; Markov random fields (MRFs); Inference; Global optimum; Markov model; Algorithm; Subjective evaluation; Statistical method; Signal processing; reconstruction; Wyner Ziv problem; Optical flow; maximum a posteriori
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2011.2133830

Within the existing reconstruction process of distributed video coding (DVC), there are two major approaches: the maximum probability reconstruction and the minimum mean square error (MMSE) reconstruction. Both of them assume that each node, a pixel in pixel domain DVC or a coefficient in transform domain DVC, is i.i.d., and reconstruct the value of each node independently by only exploiting statistical correlation between source and side-information. These kinds of models produce considerable amount of artifacts in decoded Wyner-Ziv (WZ) frames and degrade the objective performance. In this paper, we propose a context-adaptive Markov random field (MRF) reconstruction algorithm which exploits both the statistical correlation and the spatio-temporal consistency by modeling the corresponding MRF of a generic DVC architecture, and solve the inference by finding its MRF-based maximum a posteriori (MAP) estimate. The energy function of the MRF model consists of two terms: a data term measuring the statistical correlation, and a geometric regularity term enforcing local spatio-temporal structure consistency which is modeled by optical flow estimation with regard to the critical parameters under a wide variety of DVC scenarios. In case the unreliability of the derived local structure, a confidence parameter is introduced to prevent inappropriate penalizing. To find the reconstructed patch assignment with the largest expected probability in the context-adaptive MRF, the energy minimization for the MRF-based MAP estimate of the WZ frames is solved by global optimization and greedy strategies. Compared to the existing maximum probability and MMSE reconstruction with i.i.d. model, a better subjective and objective performance is validated by extensive experiments.