Model-Based Joint Bit Allocation Between Texture Videos and Depth Maps for 3-D Video Coding

• Published in: IEEE transactions on circuits and systems for video technology Vol. 21; no. 4; pp. 485 - 497
• Main Authors: Yuan, Hui, Chang, Yilin, Huo, Junyan, Yang, Fuzheng, Lu, Zhaoyang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D video coding; Algorithms; Allocations; Applied sciences; Bit rate; Cameras; Coding; Distortion; distortion model; Exact sciences and technology; Image processing; Information, signal and communications theory; joint bit allocation; Joints; Mathematical models; Pixel; rate distortion optimization; Signal processing; Studies; Surface layer; Telecommunications and information theory; Texture; Three dimensional displays; Video coding; Videos; Video coding; Performance evaluation; High precision; Rate distortion theory; rate distortion optimization; Virtual image; distortion model; 3-D video coding; Texture; Research and development; Video signal processing; Search algorithm; Constrained optimization; Bit allocation; joint bit allocation; Lagrange multiplier; Lagrangian method; Gain
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2011.2125610

In 3-D video coding, texture videos and depth maps need to be jointly coded. The distortion of texture videos and depth maps can be propagated to the synthesized virtual views. Besides coding efficiency of texture videos and depth maps, joint bit allocation between texture videos and depth maps is also an important research issue in 3-D video coding. First, we present comprehensive analyses on the impacts of the compression distortion of texture videos and depth maps on the quality of the virtual views, and then derive a concise distortion model for the synthesized virtual views. Based on this model, the joint bit allocation problem is formulated as a constrained optimization problem, and is solved by using the Lagrangian multiplier method. Experimental results demonstrate the high accuracy of the derived distortion model. Meanwhile, the rate-distortion (R-D) performance of the proposed algorithm is close to those of search-based algorithms which can give the best R-D performance, while the complexity of the proposed algorithm is lower than that of search-based algorithms. Moreover, compared with the bit allocation method using fixed texture and depth bits ratio (5:1), a maximum 1.2 dB gain can be achieved by the proposed algorithm.