基于多维分析的立体视频质量度量

Stereo Video Quality Metric Based on Multi-Dimensional Analysis.

作者信息

He Zhouyan, Xu Haiyong, Luo Ting, Liu Yi, Song Yang

机构信息

College of Science and Technology, Ningbo University, Ningbo 315211, China.

School of Mathematics and Statistics, Ningbo University, Ningbo 315211, China.

出版信息

Entropy (Basel). 2021 Aug 30;23(9):1129. doi: 10.3390/e23091129.

DOI:10.3390/e23091129

PMID:34573754

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8464717/

Abstract

Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.

摘要

近年来，立体视频已广泛应用于各种视频系统中。因此，客观立体视频质量度量（SVQM）对于提升观看体验非常必要。然而，由于立体视频中的数据维度较高，现有度量在准确性和鲁棒性方面存在一些缺陷。基于立体视频的特性，本文考虑了立体视频中多维信息的共存与交互，提出了一种基于多维分析的SVQM（MDA-SVQM）。具体而言，通过分析和比较不同维度的相关性，建立了一个时域-视角联合分解（TVJD）模型，并将立体视频帧组（sGoF）自适应地分解为不同子带。然后，根据每个子带的生成机制和物理意义，分别针对高频和低频子带提取基于直方图和基于LOID的特征，并通过回归得到sGoF质量。最后，利用时空能量加权（STEW）模型计算每个sGoF的权重，并通过对所有sGoF质量进行加权求和得到最终的立体视频质量。在两个立体视频数据库上进行的实验表明，MDA-SVQM中采用的TVJD和STEW是可信的，并且MDA-SVQM的整体性能优于几种现有的SVQM。