A complexity reduction algorithm for depth maps intra prediction on the 3D-HEVC

G. Sanchez, Mário Saldanha, Gabriel Balota, B. Zatt, M. Porto, L. Agostini
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引用次数: 24

Abstract

This paper proposes a complexity reduction algorithm for the depth maps intra prediction of the emerging 3D High Efficiency Video Coding standard (3D-HEVC). The 3D-HEVC introduces a new set of specific tools for the depth map coding that includes four Depth Modeling Modes (DMM) and these new features have inserted extra effort on the intra prediction. This extra effort is undesired and contributes to increasing the power consumption, which is a huge problem especially for embedded-systems. For this reason, this paper proposes a complexity reduction algorithm for the DMM 1, called Gradient-Based Mode One Filter (GMOF). This algorithm applies a filter to the borders of the encoded block and determines the best positions to evaluate the DMM 1, reducing the computational effort of DMM 1 process. Experimental analysis showed that GMOF is capable to achieve, in average, a complexity reduction of 9.8% on depth maps prediction, when evaluating under Common Test Conditions (CTC), with minor impacts on the quality of the synthesized views.
基于3D-HEVC的深度图内预测复杂度降低算法
针对新兴的3D高效视频编码标准(3D- hevc),提出了一种深度图帧内预测的复杂度降低算法。3D-HEVC为深度图编码引入了一套新的特定工具,其中包括四种深度建模模式(DMM),这些新功能为图像内预测增加了额外的工作量。这种额外的工作是不希望的,并且会增加功耗,这是一个巨大的问题,特别是对于嵌入式系统。为此,本文提出了一种dmm1的复杂度降低算法,称为基于梯度的模式一滤波器(GMOF)。该算法对编码块的边界进行滤波,确定对DMM - 1进行评估的最佳位置,减少了DMM - 1过程的计算量。实验分析表明,在通用测试条件(Common Test Conditions, CTC)下评估时,GMOF能够将深度图预测的复杂性平均降低9.8%,对合成视图的质量影响较小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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