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A Virtual View PSNR Estimation Method for 3-D Videos

机译:一种用于3-D视频的虚拟视图PSNR估计方法

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摘要

In three-dimensional videos (3-DVs) with -view texture videos plus -view depth maps, virtual views can be synthesized from neighboring texture videos and the associated depth maps. To evaluate the system performance or guide the rate-distortion-optimization process of 3-DV coding, the distortion/PSNR of the virtual view should be calculated by measuring the quality difference between the virtual view synthesized by compressed 3-DVs with one synthesized by uncompressed 3-DVs, which increases the complexity of a 3-DV system. In order to reduce the complexity of 3-DV system, it is better to estimate virtual view distortions/PSNR directly without rendering virtual views. In this paper, the virtual view synthesis procedure and the distortion propagation from existing views to virtual views are analyzed in detail, and then a virtual view distortion/PSNR estimation method is derived. Experimental results demonstrate that the proposed method could estimate PSNRs of virtual views accurately. The squared correlation coefficient and root of mean squared error between the estimated PSNRs by the proposed method and the actual PSNRs are 0.998 and 2.012 on average for all the tested sequences. Since the proposed method is implemented row-by-row independently, it is also friendly for parallel design. The execute time for each row of pictures with resolution is only 0.079 s, while for pictures with resolution it is only 0.155 s.
机译:在具有-view纹理视频加上-view深度图的三维视频(3-DV)中,可以根据相邻的纹理视频和关联的深度图来合成虚拟视图。为了评估系统性能或指导3-DV编码的速率失真优化过程,应通过测量压缩的3-DV合成的虚拟视图与通过压缩的3-DV合成的虚拟视图之间的质量差来计算虚拟视图的失真/ PSNR。未压缩的3-DV,这增加了3-DV系统的复杂性。为了降低3-DV系统的复杂性,最好不估计虚拟视图而直接估计虚拟视图失真/ PSNR。本文详细分析了虚拟视图的合成过程和从现有视图到虚拟视图的失真传播,然后推导了虚拟视图失真/ PSNR估计方法。实验结果表明,该方法可以准确估计虚拟视图的PSNR。对于所有测试序列,所提出的方法估计的PSNR与实际PSNR之间的相关系数平方和均方根均方根分别为0.998和2.012。由于所提出的方法是逐行独立实现的,因此对并行设计也很友好。分辨率为每行图片的执行时间仅为0.079 s,而分辨率为图片的执行时间仅为0.155 s。

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