Song Yang, Yu Mei, Jiang Gangyi, Shao Feng, Peng Zongju
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China.
PLoS One. 2017 Apr 26;12(4):e0175798. doi: 10.1371/journal.pone.0175798. eCollection 2017.
Well-performed Video quality assessment (VQA) method should be consistent with human visual systems for better prediction accuracy. In this paper, we propose a VQA method using motion-compensated temporal filtering (MCTF) and manifold feature similarity. To be more specific, a group of frames (GoF) is first decomposed into a temporal high-pass component (HPC) and a temporal low-pass component (LPC) by MCTF. Following this, manifold feature learning (MFL) and phase congruency (PC) are used to predict the quality of temporal LPC and temporal HPC respectively. The quality measures of the LPC and the HPC are then combined as GoF quality. A temporal pooling strategy is subsequently used to integrate GoF qualities into an overall video quality. The proposed VQA method appropriately processes temporal information in video by MCTF and temporal pooling strategy, and simulate human visual perception by MFL. Experiments on publicly available video quality database showed that in comparison with several state-of-the-art VQA methods, the proposed VQA method achieves better consistency with subjective video quality and can predict video quality more accurately.
性能良好的视频质量评估（VQA）方法应与人类视觉系统保持一致，以获得更高的预测准确性。在本文中，我们提出了一种使用运动补偿时域滤波（MCTF）和流形特征相似度的VQA方法。具体而言，首先通过MCTF将一组帧（GoF）分解为一个时域高通分量（HPC）和一个时域低通分量（LPC）。在此之后，分别使用流形特征学习（MFL）和相位一致性（PC）来预测时域LPC和时域HPC的质量。然后将LPC和HPC的质量度量组合为GoF质量。随后使用一种时域池化策略将GoF质量整合为整体视频质量。所提出的VQA方法通过MCTF和时域池化策略适当地处理视频中的时域信息，并通过MFL模拟人类视觉感知。在公开可用的视频质量数据库上进行的实验表明，与几种最新的VQA方法相比，所提出的VQA方法与主观视频质量具有更好的一致性，并且能够更准确地预测视频质量。
