Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding

An efficient bit allocation algorithm based on a novel view synthesis distortion model is proposed for the rate-distortion optimized coding of multiview video plus depth sequences in this paper. We decompose an input frame into nonedge blocks and edge blocks. For each nonedge block, we linearly appr...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 8 vom: 24. Aug., Seite 3254-67
1. Verfasser: Chung, Tae-Young (VerfasserIn)
Weitere Verfasser: Sim, Jae-Young, Kim, Chang-Su
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2014
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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245 1 0 |a Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding 
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520 |a An efficient bit allocation algorithm based on a novel view synthesis distortion model is proposed for the rate-distortion optimized coding of multiview video plus depth sequences in this paper. We decompose an input frame into nonedge blocks and edge blocks. For each nonedge block, we linearly approximate its texture and disparity values, and derive a view synthesis distortion model, which quantifies the impacts of the texture and depth distortions on the qualities of synthesized virtual views. On the other hand, for each edge block, we use its texture and disparity gradients for the distortion model. In addition, we formulate a bit-rate allocation problem in terms of the quantization parameters for texture and depth data. By solving the problem, we can optimally divide a limited bit budget between the texture and depth data, in order to maximize the qualities of synthesized virtual views, as well as those of encoded real views. Experimental results demonstrate that the proposed algorithm yields the average PSNR gains of 1.98 and 2.04 dB in two-view and three-view scenarios, respectively, as compared with a benchmark conventional algorithm 
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700 1 |a Kim, Chang-Su  |e verfasserin  |4 aut 
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