With the rapid development of Internet technology, the application of screen content image(SCI) in the network is becoming increasingly widespread, and the issue of objective quality assessment has become a research hotspot. A full-reference SCI quality assessment method integrating text integrity and image perception features was proposed. To address the rich text characteristics of SCI, optical character recognition(OCR) technology was utilized to extract text content, and character error rate(CER) was calculated as the text integrity feature. Simultaneously, multiple quality perception features, including spatial, structural, and information-theoretic features, were incorporated. Random forest was employed to learn the adaptive weights of the features, and support vector regression(SVR) was utilized to establish the mapping relationship between the fused features and subjective quality scores. The experimental results on two widely used datasets, SCID and SIQAD, demonstrate that the proposed method achieves superior performance. Compared with existing competitive methods, the prediction accuracy of the proposed method on the SCID dataset improves by more than 2%, which more accurately reflects the perception characteristics of the human visual system for screen content.
ZHANG Yin
,
YANG Chao
,
AN Ping
,
HUANG Xinpeng
. Full-Reference Screen Content Image Quality Assessment via Text-Visual Fusion[J]. Journal of Applied Sciences, 2026
, 44(4)
: 657
-668
.
DOI: 10.3969/j.issn.0255-8297.2026.04.010
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