Toward Accurate Quality Estimation of Screen Content Pictures With Very Sparse Reference Information

Zhifang XIA, Ke GU, Shiqi WANG, Hantao LIU, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

16 Citations (Scopus)


The screen content (SC) pictures, such as webpages, serve as a visible and convenient medium to well-represent the Internet information, and therefore, the visual quality of SC pictures is highly significant and has attained a growing amount of attention. Accurate quality evaluation of SC pictures not only provides the fidelity of the conveyed information, but also contributes to the improvement of the user experience. In practical applications, a reliable estimation of SC pictures plays a considerably critical role for the optimization of the processing systems as the guidance. Based on these motivations, this paper proposes a novel method for precisely assessing the quality of SC pictures using very sparse reference information. Specifically, the proposed quality method separately extracts the macroscopic and microscopic structures, followed by comparing the differences of macroscopic and microscopic features between a pristine SC picture and its corrupted version to infer the overall quality score. By studying the feature histogram for dimensionality reduction, the proposed method merely requires two features as the reference information that can be exactly embedded in the file header with very few bits. Experiments manifest the superiority of our algorithm as compared with state-of-the-art relevant quality metrics when applied to the visual quality evaluation of SC pictures.
Original languageEnglish
Pages (from-to)2251-2261
JournalIEEE Transactions on Industrial Electronics
Issue number3
Early online date22 Mar 2019
Publication statusPublished - Mar 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Science Foundation of China under Grant 61703009 and Grant 61527804, in part by the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology under Grant 2017QNRC001, and in part by the Young Top-Notch Talents Team Program of Beijing Excellent Talents Funding under Grant 2017000026833ZK40.


  • Macroscopic/microscopic structure
  • quality estimation
  • screen content (SC) picture
  • sparse reference


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