An efficient temporal distortion measure of videos based on spacetime texture. (October 2017)

• Record Type: Journal Article
• Authors: Peng, Peng; Liao, Danping; Li, Ze-Nian
• Is Part Of: Pattern recognition. Volume 70(2017:Oct.)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-10
• Subjects: Video quality assessment -- Temporal distortion measure -- Spacetime texture -- Spatio-temporal oriented energy (SOE) -- Visual saliency; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.04.031
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: It introduces spacetime texture for VQA as a good alternative to optical flow. It unifies motion tuning and visual saliency on spacetime texture for VQA. It has high correlation with subjective quality. It is consistent across various distortion types and viewing conditions. It has high computational efficiency. Abstract: Objective video quality assessment (VQA) plays an important role in controlling video quality. Most of the existing VQA methods measure motion-related temporal distortion based on optical-flow methods, which are not consistently reliable in modeling general visual dynamics. This paper presents a full-reference temporal distortion measure based on spacetime texture, a uniform and distributive descriptor of a broad set of spacetime structures. Our method measures the distortion of spacetime texture in video with a motion-tuning strategy, which effectively captures temporal distortion along the motion trajectories. Then it estimates self-information based visual saliency for spatial pooling by reusing the motion descriptors. We evaluated our method on two public VQA databases with a wide variety of distortion types, in which the videos were viewed on a large screen or mobile devices. The results show that our method correlates highly with the subjective quality and has high computational efficiency.