Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139731
Title: Evaluating quality of screen content images via structural variation analysis
Authors: Gu, Ke
Qiao, Junfei
Min, Xiongkuo
Yue, Guanghui
Lin, Weisi
Thalmann, Daniel
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Gu, K., Qiao, J., Min, X., Yue, G., Lin, W., & Thalmann, D. (2018). Evaluating quality of screen content images via structural variation analysis. IEEE Transactions on Visualization and Computer Graphics, 24(10), 2689-2701. doi:10.1109/TVCG.2017.2771284
Journal: IEEE Transactions on Visualization and Computer Graphics
Abstract: With the quick development and popularity of computers, computer-generated signals have drastically invaded into our daily lives. Screen content image is a typical example, since it also includes graphic and textual images as components as compared with natural scene images which have been deeply explored, and thus screen content image has posed novel challenges to current researches, such as compression, transmission, display, quality assessment, and more. In this paper, we focus our attention on evaluating the quality of screen content images based on the analysis of structural variation, which is caused by compression, transmission, and more. We classify structures into global and local structures, which correspond to basic and detailed perceptions of humans, respectively. The characteristics of graphic and textual images, e.g., limited color variations, and the human visual system are taken into consideration. Based on these concerns, we systematically combine the measurements of variations in the above-stated two types of structures to yield the final quality estimation of screen content images. Thorough experiments are conducted on three screen content image quality databases, in which the images are corrupted during capturing, compression, transmission, etc. Results demonstrate the superiority of our proposed quality model as compared with state-of-the-art relevant methods.
URI: https://hdl.handle.net/10356/139731
ISSN: 1077-2626
DOI: 10.1109/TVCG.2017.2771284
Rights: © 2017 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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