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Depth video spatial and temporal correlation enhancement algorithm based on just noticeable rendering distortion model

机译:基于可观的渲染失真模型的深度视频时空相关增强算法

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摘要

Spatial and temporal inconsistency of depth video deteriorates encoding efficiency in three dimensional video systems. A depth video processing algorithm based on human perception is presented. Firstly, a just noticeable rendering distortion (JNRD) model is formulated by combining the analyses of the influence of depth distortion on virtual view rendering with human visual perception characteristics. Then, depth video is processed based on the JNRD model from two aspects, spatial and temporal correlation enhancement. During the process of spatial correlation enhancement, depth video is segmented into edge, foreground, and background regions, and smoothened by Gaussian and mean filters. The operations of the temporal correlation enhancement include temporal-spatial transpose (TST), temporal smoothing filter and inverse TST. Finally, encoding and virtual view rendering experiments are conducted to evaluate the proposed algorithm. Experimental results show that the proposed algorithm can greatly reduce the bit rate while it maintains the quality of virtual view. (C) 2015 Elsevier Inc. All rights reserved.
机译:在三维视频系统中,深度视频的空间和时间不一致会降低编码效率。提出了一种基于人类感知的深度视频处理算法。首先,通过将深度失真对虚拟视图渲染的影响与人类视觉感知特征的分析相结合,建立了一个可观的渲染失真(JNRD)模型。然后,从空间和时间相关性增强两个方面,基于JNRD模型处理深度视频。在空间相关增强的过程中,深度视频被分割为边缘,前景和背景区域,并通过高斯和均值滤波器进行平滑处理。时间相关性增强的操作包括时间空间转置(TST),时间平滑滤波器和逆TST。最后,进行编码和虚拟视图渲染实验以评估该算法。实验结果表明,该算法在保持虚拟视图质量的同时,可以大大降低比特率。 (C)2015 Elsevier Inc.保留所有权利。

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