FQI: feature‐based reduced‐reference image quality assessment method for screen content images
Open Access
- 29 April 2019
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Image Processing
- Vol. 13 (7), 1170-1180
- https://doi.org/10.1049/iet-ipr.2018.5496
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.Keywords
This publication has 39 references indexed in Scilit:
- Perceptual Quality Assessment of Screen Content ImagesIEEE Transactions on Image Processing, 2015
- Study on subjective quality assessment of Screen Content ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label TransferIEEE Transactions on Circuits and Systems for Video Technology, 2015
- The Analysis of Image Contrast: From Quality Assessment to Automatic EnhancementIEEE Transactions on Cybernetics, 2015
- VSI: A Visual Saliency-Induced Index for Perceptual Image Quality AssessmentIEEE Transactions on Image Processing, 2014
- No-Reference Image Quality Assessment through SIFT IntensityApplied Mathematics & Information Sciences, 2014
- An Image Visual Quality Assessment Method Based on SIFT FeaturesJournal of Pattern Recognition Research, 2013
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Image Quality Assessment: From Error Visibility to Structural SimilarityIEEE Transactions on Image Processing, 2004
- The Laplacian Pyramid as a Compact Image CodeIEEE Transactions on Communications, 1983