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 | Schools: | School of Computer Science and Engineering | Rights: | © 2017 IEEE. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Journal Articles |
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