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Optimizing rate allocation between multiview videos and associated depth maps with quantization-based virtual view distortion model and genetic algorithm

[+] Author Affiliations
Chuan Ge

Shandong University, School of Information Science and Engineering, No. 27 Shandanan Road, Jinan 250101, China

Ju Liu

Shandong University, School of Information Science and Engineering, No. 27 Shandanan Road, Jinan 250101, China

Hisense State Key Laboratory of Digital Multi-Media Technology, No. 11 Jiangxi Road, Qingdao 266061, China

Hui Yuan

Shandong University, School of Information Science and Engineering, No. 27 Shandanan Road, Jinan 250101, China

Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology, 865 Changning Road, Shanghai 200050, China

J. Electron. Imaging. 23(6), 063016 (Dec 17, 2014). doi:10.1117/1.JEI.23.6.063016
History: Received May 13, 2014; Accepted November 11, 2014
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Abstract.  In three-dimensional video coding (3-DVC), it is reasonable to allocate different coding bits for multiview videos and associated depth maps because of their different characteristics to meet the bits’ restraints of bandwidth/storage. We first propose a virtual view average distortion model. Then, based on the quantization parameter (QP), the average distortion-QP models and sum bitrate-QP models are proposed to depict the average distortions and sum bitrates of the referenced views for multiview videos and depth maps. Finally, the 3-DVC bit allocation problem is converted as a constrained optimization problem, which is solved by a genetic algorithm to search for the optimal QP pair. Experimental results demonstrate the effectiveness of our proposed models. Since the bit allocation scheme takes the performance of synthesized view and bitrate utilization into consideration, the absolute difference between the constraint and the actual coding bitrates (referred to as “rate inaccuracy”) of the proposed method is only 7.405% on average, which greatly outperforms the fixed 51 ratio-based method with a rate inaccuracy of 19.103% and the planar model-based method with a rate inaccuracy of 20.556%. Compared with these two methods, our proposed method can achieve a maximum 1.951-dB gain under the same bitrates constraint.

© 2014 SPIE and IS&T

Citation

Chuan Ge ; Ju Liu and Hui Yuan
"Optimizing rate allocation between multiview videos and associated depth maps with quantization-based virtual view distortion model and genetic algorithm", J. Electron. Imaging. 23(6), 063016 (Dec 17, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.063016


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