FQI: feature‐based reduced‐reference image quality assessment method for screen content images. Issue 7 (29th April 2019)

• Record Type: Journal Article
• Authors: Rahul, Kumar; Tiwari, Anil Kumar
• Is Part Of: IET image processing. Volume 13:Issue 7(2019)
• Page Start: 1170
• Page End: 1180
• Publication Date: 2019-04-29
• Subjects: feature extraction -- image matching; FQI -- screen content images -- feature‐quality‐index -- distorted image -- preserved features -- feature matching process -- reduced number -- reduced‐distance method -- matched features -- feature‐based reduced‐reference image quality assessment method -- human visual system -- reduced feature extraction -- distance calculations -- SIQAD benchmark database -- QACS benchmark database -- full‐reference IQA techniques; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5496
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.