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Fast depth map mode decision based on depth-texture correlation and edge classification for 3D-HEVC

机译:基于深度纹理相关性和边缘分类的3D-HEVC快速深度图模式决策

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摘要

The 3D extension of High Efficiency Video Coding (3D-HEVC) has been adopted as the emerging 3D video coding standard to support the multi-view video plus depth map (MVD) compression. In the joint model of 3D-HEVC design, the exhaustive mode decision is required to be checked all the possible prediction modes and coding levels to find the one with least rate distortion cost in depth map coding. Furthermore, new coding tools (such as depth-modeling mode (DMM) and segment-wise depth coding (SDC)) are exploited for the characteristics of depth map to improve the coding efficiency. These achieve the highest possible coding efficiency to code depth map, but also bring a significant computational complexity which limits 3D-HEVC from real-time applications. In this paper, we propose a fast depth map mode decision algorithm for 3D-HEVC by jointly using the correlation of depth map-texture video and the edge information of depth map. Since the depth map and texture video represent the same scene at the same time instant (they have the same motion characteristics), it is not efficient to use all the prediction modes and coding levels in depth map coding. Therefore, we can skip some specific prediction modes and depth coding levels rarely used in corresponding texture video. Meanwhile, the depth map is mainly characterized by sharp object edges and large areas of nearly constant regions. By fully exploiting these characteristics, we can skip some prediction modes which are rarely used in homogeneity regions based on the edge classification. Experimental results show that the proposed algorithm achieves considerable encoding time saving while maintaining almost the same rate-distortion (RD) performance as the original 3D-HEVC encoder. (C) 2017 Elsevier Inc. All rights reserved.
机译:高效视频编码(3D-HEVC)的3D扩展已被用作新兴的3D视频编码标准,以支持多视点视频加深度图(MVD)压缩。在3D-HEVC设计的联合模型中,要求穷举模式决策要检查所有可能的预测模式和编码级别,以找到深度图编码中速率失真成本最低的一种。此外,针对深度图的特性,采用了新的编码工具(例如,深度建模模式(DMM)和分段深度编码(SDC)),以提高编码效率。这些实现了对编码深度图的最高可能的编码效率,但是还带来了显着的计算复杂性,从而限制了实时应用程序中的3D-HEVC。本文结合深度图纹理视频的相关性和深度图的边缘信息,提出了一种快速的3D-HEVC深度图模式决策算法。由于深度图和纹理视频在同一时刻表示相同的场景(它们具有相同的运动特性),因此在深度图编码中使用所有预测模式和编码级别效率不高。因此,我们可以跳过一些在相应纹理视频中很少使用的特定预测模式和深度编码级别。同时,深度图的主要特征是尖锐的物体边缘和几乎恒定区域的大面积。通过充分利用这些特征,我们可以跳过基于边缘分类在均匀性区域中很少使用的一些预测模式。实验结果表明,该算法可节省大量编码时间,同时保持与原始3D-HEVC编码器几乎相同的速率失真(RD)性能。 (C)2017 Elsevier Inc.保留所有权利。

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  • 作者单位

    Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, 5 Dongfeng Rd, Zhengzhou 450002, Peoples R China;

    Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, 5 Dongfeng Rd, Zhengzhou 450002, Peoples R China;

    China Natl Digital Switching Syst Engn Technol R&, Zhengzhou 450002, Peoples R China|Henan Inst Engn, Coll Comp, Zhengzhou 451191, Peoples R China;

    Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, 5 Dongfeng Rd, Zhengzhou 450002, Peoples R China;

    Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Peoples R China;

    Zhengzhou Univ Light Ind, Coll Comp & Commun Engn, 5 Dongfeng Rd, Zhengzhou 450002, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    3D-HEVC; Depth map; Mode decision; Edge classification;

    机译:3D-HEVC;深度图;模式决策;边缘分类;

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