ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment
This work addresses image quality assessment for applications like image processing and compression, representing an incremental improvement over existing methods.
The paper tackled the problem of full-reference image quality assessment by developing ReSIFT, a method based on SIFT descriptor matching with reliability-weighted feature maps. The result showed that ReSIFT achieved the best performance in terms of Pearson and Spearman correlation on the LIVE and LIVE Multiply Distorted databases, outperforming 11 state-of-the-art estimators overall and in specific distortion categories like compression, noise, and blur.
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.