一种基于低秩张量建模的自由视点视频深度视频编码方案
A Versatile Depth Video Encoding Scheme Based on Low-rank Tensor Modeling for Free Viewpoint Video
AI总结：
本文提出了一种基于低秩张量分解和HEVC帧内编码的低复杂度深度视频压缩方案，通过将深度序列表示为高阶张量并利用CP分解获取场景几何的低秩近似，在保证渲染质量的同时实现了显著的比特率增益。
摘要：
深度序列的压缩质量损失决定了自由视点视频中视图合成的质量。HEVC的3D扩展中的深度图帧内预测应用带有辅助深度建模模式（DMMs）的帧内模式，以更好地保留深度边缘并处理运动不连续性。尽管此类模式实现了高效压缩，但代价是非常高的编码复杂度。在深度编码中跳过传统帧内编码模式和DMMs限制了HEVC在3D显示应用中的实际适用性。在本文中，我们引入了一种基于低秩张量分解和HEVC帧内编码的低复杂度深度视频压缩方案。该方案通过将深度序列紧凑地表示为高阶张量来利用空间和时间冗余。通过交替最小二乘法将张量分解为遵循CANDECOMP PARAFAC（CP）分解的一组因子矩阵，给出了场景几何的低秩近似。此外，利用HEVC帧内预测对因子矩阵进行压缩，通过灵活调整比特率、改变张量分解秩和量化参数来支持任意目标精度。结果表明，所提出的方法通过在低秩近似表示中高效压缩深度平面，实现了显著的比特率增益。所提出的算法被应用于编码基准Ballet和Breakdancing序列的深度图。解码后的深度序列用于多视图视频系统中的视图合成，保持了适当的渲染质量。
英文摘要：
The compression quality losses of depth sequences determine quality of view synthesis in free-viewpoint video. The depth map intra prediction in 3D extensions of the HEVC applies intra modes with auxiliary depth modeling modes (DMMs) to better preserve depth edges and handle motion discontinuities. Although such modes enable high efficiency compression, but at the cost of very high encoding complexity. Skipping conventional intra coding modes and DMMs in depth coding limits practical applicability of the HEVC for 3D display applications. In this paper, we introduce a novel low-complexity scheme for depth video compression based on low-rank tensor decomposition and HEVC intra coding. The proposed scheme leverages spatial and temporal redundancy by compactly representing the depth sequence as a high-order tensor. Tensor factorization into a set of factor matrices following CANDECOMP PARAFAC (CP) decomposition via alternating least squares give a low-rank approximation of the scene geometry. Further, compression of factor matrices with HEVC intra prediction support arbitrary target accuracy by flexible adjustment of bitrate, varying tensor decomposition ranks and quantization parameters. The results demonstrate proposed approach achieves significant rate gains by efficiently compressing depth planes in low-rank approximated representation. The proposed algorithm is applied to encode depth maps of benchmark Ballet and Breakdancing sequences. The decoded depth sequences are used for view synthesis in a multi-view video system, maintaining appropriate rendering quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
