Screen Content Video Quality Assessment : Subjective and Objective Study

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Shan Cheng
  • Huanqiang Zeng
  • Jing Chen
  • Jianqing Zhu
  • Kai-Kuang Ma

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9178481
Pages (from-to)8636-8651
Journal / PublicationIEEE Transactions on Image Processing
Volume29
Online published26 Aug 2020
Publication statusPublished - 2020

Abstract

In this article, we make the first attempt to study the subjective and objective quality assessment for the screen content videos (SCVs). For that, we construct the first large-scale video quality assessment (VQA) database specifically for the SCVs, called the screen content video database (SCVD). This SCVD provides 16 reference SCVs, 800 distorted SCVs, and their corresponding subjective scores, and it is made publicly available for research usage. The distorted SCVs are generated from each reference SCV with 10 distortion types and 5 degradation levels for each distortion type. Each distorted SCV is rated by at least 32 subjects in the subjective test. Furthermore, we propose the first full-reference VQA model for the SCVs, called the spatiotemporal Gabor feature tensor-based model (SGFTM), to objectively evaluate the perceptual quality of the distorted SCVs. This is motivated by the observation that 3D-Gabor filter can well stimulate the visual functions of the human visual system (HVS) on perceiving videos, being more sensitive to the edge and motion information that are often-encountered in the SCVs. Specifically, the proposed SGFTM exploits 3D-Gabor filter to individually extract the spatiotemporal Gabor feature tensors from the reference and distorted SCVs, followed by measuring their similarities and later combining them together through the developed spatiotemporal feature tensor pooling strategy to obtain the final SGFTM score. Experimental results on SCVD have shown that the proposed SGFTM yields a high consistency on the subjective perception of SCV quality and consistently outperforms multiple classical and state-of-the-art image/video quality assessment models.

Research Area(s)

  • Screen content videos (SCVs), video quality assessment (VQA), subjective assessment, objective measurement, spatiotemporal feature tensor

Citation Format(s)

Screen Content Video Quality Assessment : Subjective and Objective Study. / Cheng, Shan; Zeng, Huanqiang; Chen, Jing; Hou, Junhui; Zhu, Jianqing; Ma, Kai-Kuang.

In: IEEE Transactions on Image Processing, Vol. 29, 9178481, 2020, p. 8636-8651.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal