FQI: feature-based reduced-reference image quality assessment method for screen content images

• Published in: IET image processing Vol. 13; no. 7; pp. 1170 - 1180
• Main Authors: Rahul, Kumar, Tiwari, Anil Kumar
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2019; Wiley
• Subjects: distance calculations; distorted image; feature extraction; feature matching process; feature‐based reduced‐reference image quality assessment method; feature‐quality‐index; FQI; full‐reference IQA techniques; human visual system; image matching; matched features; preserved features; QACS benchmark database; reduced feature extraction; reduced number; reduced‐distance method; Research Article; screen content images; SIQAD benchmark database; feature matching process; human visual system; feature-based reduced-reference image quality assessment method; full-reference IQA techniques; matched features; reduced-distance method; distorted image; reduced feature extraction; distance calculations; image matching; SIQAD benchmark database; reduced number; preserved features; feature extraction; QACS benchmark database; feature-quality-index; screen content images; FQI
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5496

In this study, a reduced-reference image-quality-assessment (IQA) method for screen content images, named as feature-quality-index (FQI) is proposed. The proposed method is based on the fact that the human visual system is more sensitive towards change in features than intensity or structure. Reduced features from the reference and distorted images are first extracted. In order to find the preserved features in the distorted image, a feature matching process with a reduced number of distance calculations is proposed, namely reduced-distance method. To reflect the importance of the matched features and their distance, the inner product between the normalised scale and distance vector is obtained. Extensive comparisons are performed on two available benchmark databases namely SIQAD and QACS, with eight reduced-reference, and nine full-reference state-of-the-art IQA techniques to demonstrate the consistency, accuracy, and robustness of the proposed FQI. The subjective evaluation of mean opinion score shows that FQI outperforms the current state-of-the-art IQA techniques.