Perceptual video quality assessment and analysis using adaptive content dynamics

Video quality;Perception;Video coding;Distortion;Video streaming;Quality assessment

Aabed, Mohammed A. ; AlRegib, Ghassan Electrical and Computer Engineering Yezzi, Anthony Anderson, David Moore, Elliot Bras, Berdinus A. ; AlRegib, Ghassan

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Video quality;    Perception;    Video coding;    Distortion;    Video streaming;    Quality assessment;   

Others  :  https://smartech.gatech.edu/bitstream/1853/58222/1/AABED-DISSERTATION-2017.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

With the growth of mobile data services and bandwidth, several applications and streaming services have emerged that made video quality and technologies important fields of research and development. Understanding perceptual video quality can be achieved through understanding and tightly linking the perceptual nature of the human visual system and varying characteristics and dynamics of video contents. In this dissertation, the objective of the proposed research is to investigate perceptual quality assessment and analysis of videos subject to different types of distortion. We propose utilizing adaptive content dynamics to examine the impact of different error sources on the perceptual quality of the video. We design perceptual video quality estimators using novel handcrafted features inspired by the human visual properties. We explore new feature spaces and utilize them to capture varying video dynamics as experienced by our visual perception. Specifically, we introduce a new framework for perceptual video quality using pixel-level optical flow maps where we propose a motion processing procedure inspired by the hierarchical processing of motion in the visual cortex. Furthermore, we propose another perceptual video quality assessment approach by examining the varying properties of the tempospatial power spectrum. Using the power spectrum, we design a novel sensitivity measure to capture the impact of distortions on visual perception. This work includes a full-reference computationally efficient framework that captures both spatial and temporal characteristics in the frequency domain. We also examine the performance of various statistical moments and pooling strategies, at both spatial and temporal levels, with different visual feature maps. This aims at revealing the optimal pooling strategies most correlated with visual perception for every feature space with respect to different distortions.