ReSIFT: Reliability-weighted sift-based image quality assessment
- 19 August 2016
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2016 IEEE International Conference on Image Processing (ICIP)
- p. 2047-2051
- https://doi.org/10.1109/icip.2016.7532718
Abstract
This paper presents a full-reference image quality estimator based on SIFT descriptor matching over reliability-weighted feature maps. Reliability assignment includes a smoothing operation, a transformation to perceptual color domain, a local normalization stage, and a spectral residual computation with global normalization. The proposed method ReSIFT is tested on the LIVE and the LIVE Multiply Distorted databases and compared with 11 state-of-the-art full-reference quality estimators. In terms of the Pearson and the Spearman correlation, ReSIFT is the best performing quality estimator in the overall databases. Moreover, ReSIFT is the best performing quality estimator in at least one distortion group in compression, noise, and blur category.Keywords
This publication has 7 references indexed in Scilit:
- 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
- Objective quality assessment of multiply distorted imagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- A new object based quality metric based on SIFT and SSIMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- SR-SIM: A fast and high performance IQA index based on spectral residualPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Saliency Detection: A Spectral Residual ApproachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004