Reduced-Reference Quality Assessment of Screen Content Images

• Published in: IEEE transactions on circuits and systems for video technology Vol. 28; no. 1; pp. 1 - 14
• Main Authors: Wang, Shiqi, Gu, Ke, Zhang, Xinfeng, Lin, Weisi, Ma, Siwei, Gao, Wen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Feature extraction; Human behavior; Image edge detection; Image quality; Image quality assessment (IQA); Interactive systems; Prediction algorithms; Quality; Quality assessment; reduced reference (RR); Remote computing; screen content images (SCIs); Uncertainty; Visual perception driven algorithms; Visualization
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2016.2602764

The screen content images (SCIs) quality influences the user experience and the interactive performance of remote computing systems. With numerous approaches proposed to evaluate the quality of natural images, much less work has been dedicated to reduced-reference image quality assessment (RR-IQA) of SCIs. Here, we propose an RR-IQA method from the perspective of SCI visual perception. In particular, the quality of the distorted SCI is evaluated by comparing a set of extracted statistical features that consider both primary visual information and unpredictable uncertainty. A unique property that differentiates the proposed method from previous RR-IQA methods for natural images is the consideration of behaviors when human subjects view the screen content, which motivates us to establish the perceptual model according to the distinct properties of SCIs. Validations based on the screen content IQA database show that the proposed algorithm provides accurate predictions across a wide range of SCI distortions with negligible transmission overhead.