Blind quality assessment of screen content images via edge histogram descriptor and statistical moments.

Farhadi Tolie, Hamidreza; Faraji, Mohammad Reza; Qi, Xiaojun

Abstract

With the growth in utilizing desktop sharing and remote control applications in recent years - for many purposes, like online education and remote working - quality assessment (QA) of screen images has become a hot topic. It could be used to enhance the user's quality experience. Currently, most screen image QA methods require a reference image, and the existing blind/no-reference methods do not consider both the image's content and chrominance degradations. This paper proposes a novel blind quality assessment method for screen content images (SCIs) through block-based content representation, which extracts content- and chromatic-based features on local, semi-global and global scales. Our proposed edge histogram descriptor- and statistical moment-based (EHDSM) method divides the image into 16 blocks, and then describes each block using its local edge and semi-global chrominance features. It also takes the global chrominance features into account to investigate how the image's color information is changed in the presence of chrominance distortions. Local features are extracted using edge histogram descriptor, while the semi-global and global features are measured by computing the statistical moments. Next, the quality assessment is achieved by training a support vector regression (SVR) model. Extensive experiments on three commonly used SCI datasets have verified the superiority of our proposed EHDSM method compared with the state-of-the-art blind screen content image quality assessment methods.

Citation

FARHADI TOLIE, H., FARAJI, M.R. and QI, X. [2023]. Blind quality assessment of screen content images via edge histogram descriptor and statistical moments. Visual computer [online], (online ahead of print). Available from: https://doi.org/10.1007/s00371-023-03108-1

• Journal Article Type: Article
• Acceptance Date: Sep 11, 2023
• Online Publication Date: Oct 1, 2023
• Deposit Date: Mar 30, 2024
• Publicly Available Date: Oct 2, 2024
• Journal: Visual computer
• Print ISSN: 0178-2789
• Electronic ISSN: 1432-2315
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s00371-023-03108-1
• Keywords: Image quality assessment (IQA); Screen content image (SCI); Edge histogram descriptor (EHD); Image content descriptor; Chrominance features
• Public URL: https://rgu-repository.worktribe.com/output/2098933