低复杂度3D-HEVC编码的自适应视图合成优化

Q3 Computer Science
Songchao Tan , Siwei Ma , Shanshe Wang , Shiqi Wang , Xinfeng Zhang , Wen Gao
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引用次数: 0

摘要

深度压缩在具有典型纹理加深度表示的三维视频编码中起着重要作用。在本文中,为了降低深度图的编码复杂度,我们提出了一种低复杂度的自适应视图合成优化方案,用于高效视频编码(3D-HEVC)标准的3D扩展。更具体地说,我们根据深度图压缩对渲染合成视图质量的影响来区分编码树单元(CTU),并将其分为两类,包括基于合成视图失真变化(SVDC)的编码树单元和基于视图合成失真估计(VSDE)的编码树单元。通过这种方式,我们可以动态地区分CTU,并应用不同的率失真优化策略。此外,对于基于VSDE的CTU,提出了一种新的失真模型,根据深度失真和纹理特征来推断合成视图的失真。因此,我们可以在深度图编码的率失真性能和计算复杂性之间实现良好的权衡。实验结果还证实,与3D-HEVC平台中最先进的方案相比,所提出的方案在降低编码复杂度的同时具有可忽略的率失真性能损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive view synthesis optimization for low complexity 3D-HEVC encoding

Depth compression plays an important role in 3D video coding with the typical texture-plus-depth representation. In this paper, to reduce the encoding complexity of depth map, we propose a low complexity adaptive View Synthesis Optimization scheme for the 3D extension of high efficiency video coding (3D-HEVC) standard. More specifically, we distinguish the coding tree units (CTUs) based on the influence of depth map compression on the quality of rendered synthesized view, and classify them into two categories, including synthesized view distortion change (SVDC) based and view synthesis distortion estimation (VSDE) based CTUs. In this manner, we can dynamically distinguish the CTUs and apply different rate-distortion optimization strategies. Moreover, for VSDE based CTUs, a new distortion model is proposed to infer the distortion of the synthesized view based on the depth distortion and texture characteristics. As such, we can achieve a good trade-off between the rate-distortion performance and computational complexity for depth map coding. Experimental results also confirm that the proposed scheme is effective in reducing the encoding complexity with ignorable rate-distortion performance loss compared with the state-of-the-art scheme in the 3D-HEVC platform.

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来源期刊
Journal of Visual Languages and Computing
Journal of Visual Languages and Computing 工程技术-计算机:软件工程
CiteScore
1.62
自引率
0.00%
发文量
0
审稿时长
26.8 weeks
期刊介绍: The Journal of Visual Languages and Computing is a forum for researchers, practitioners, and developers to exchange ideas and results for the advancement of visual languages and its implication to the art of computing. The journal publishes research papers, state-of-the-art surveys, and review articles in all aspects of visual languages.
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