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

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 a...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2011-04, Vol.21 (4), p.485-497
Main Authors: Yuan, Hui, Chang, Yilin, Huo, Junyan, Yang, Fuzheng, Lu, Zhaoyang
Format: Article
Language:English
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
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Description
Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2011.2125610