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

机译:用于3D视频编码的纹理视频和深度图之间基于模型的联合位分配

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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.
机译:在3-D视频编码中,纹理视频和深度图需要联合编码。纹理视频和深度图的失真可以传播到合成的虚拟视图。除了纹理视频和深度图的编码效率之外,纹理视频和深度图之间的联合比特分配也是3-D视频编码中的重要研究问题。首先,我们对纹理视频和深度图的压缩失真对虚拟视图质量的影响进行全面分析,然后为合成的虚拟视图导出简洁的失真模型。在此模型的基础上,将联合比特分配问题公式化为约束优化问题,并使用拉格朗日乘数法加以解决。实验结果证明了导出的失真模型的高精度。同时,该算法的R-D率失真性能接近于基于搜索的算法,R-D性能最佳,而其复杂度却低于基于搜索的算法。此外,与使用固定纹理和深度位比(5:1)的位分配方法相比,该算法可以实现最大1.2 dB的增益。

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