Title | ||
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Reconstruction for Distributed Video Coding: A Context-Adaptive Markov Random Field Approach |
Abstract | ||
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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. |
Year | DOI | Venue |
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2011 | 10.1109/TCSVT.2011.2133830 | IEEE Trans. Circuits Syst. Video Techn. |
Keywords | Field | DocType |
reconstruction algorithm,video coding,objective performance,context-adaptive markov random field,mrf model,existing reconstruction process,mmse reconstruction,statistical correlation,dvc scenario,generic dvc architecture,maximum probability reconstruction,domain dvc,reconstruction,markov processes,energy minimization,pixel,image reconstruction,maximum likelihood estimation,minimum mean square error,maximum a posteriori,encoding,global optimization,indexing terms,decoding,quantization,correlation | Iterative reconstruction,Markov process,Global optimization,Pattern recognition,Computer science,Markov random field,Minimum mean square error,Reconstruction algorithm,Artificial intelligence,Maximum a posteriori estimation,Quantization (signal processing) | Journal |
Volume | Issue | ISSN |
21 | 8 | 1051-8215 |
Citations | PageRank | References |
7 | 0.52 | 30 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yongsheng Zhang | 1 | 204 | 43.58 |
Hongkai Xiong | 2 | 512 | 82.84 |
Zhihai He | 3 | 1544 | 114.45 |
Yu Song | 4 | 356 | 52.74 |
Chang Wen Chen | 5 | 2973 | 263.89 |