Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the frame-level clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone mono-view depth sequence coder, which preserves edges implicitly by limiting quantization to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree-based decomposition (BTBD) technique. The BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modeling for context-adaptive arithmetic coding. Compared with the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames.

中文翻译：

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具有分层分区和空间域量化的深度序列编码

3D-HEVC 中的深度编码由于块级边缘逼近而使对象形状变形，并且由于深度数据中的帧级聚类趋势，缺乏有效的技术来利用统计冗余来以接近无损的质量获得更高的编码增益。本文提出了一种独立的单视图深度序列编码器，它通过将量化限制到空间域来隐式保留边缘，并使用一种新颖的基于二叉树的分解 (BTBD) 技术有效地利用帧级聚类趋势。BTBD 可以使用更少的块级预测/编码模式和更简单的上下文建模来有效地利用帧级语法、运动分量和残差中的统计冗余，用于上下文自适应算术编码。与 3D-HEVC 中的深度编码器相比，