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Reduced-reference image quality assessment through SIFT intensity ratio

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Abstract

Scale invariant feature transform (SIFT) points are scale-space extreme points, representing local minutiae features in the Gaussian scale space. SIFT intensity ratio (SIR), as a novel reduced-reference metric, is feasible to assess various common distortions without the prior knowledge of distortion types. It describes relative changes in the number of SIFT points between a test image and its corresponding reference image. SIFT points in the metric are detected in the first octave of the difference-of-Gaussian scale space under certain preprocessings: neighborhood enhancement through a Laplacian operator to sharpen isolated points and thin edges, reducing false SIFT points; double-size image magnification through linear interpolation to amplify distortion effects, improving its sensitivity to image distortions. Experimental results demonstrate that SIR is superior to existing classic reduced-reference metrics, and can be used to assess different distortions.

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Acknowledgments

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and constructive suggestions. This paper is jointly supported by the National Natural Science Foundation of China (No. 61379101, No. 51104157) and the Natural Science Foundation of Jiangsu Province (No. BK20130209).

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  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu, China

    Tongfeng Sun, Shifei Ding & Wei Chen

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  1. Tongfeng Sun
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Correspondence to Tongfeng Sun.

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Sun, T., Ding, S. & Chen, W. Reduced-reference image quality assessment through SIFT intensity ratio. Int. J. Mach. Learn. & Cyber. 5, 923–931 (2014). https://doi.org/10.1007/s13042-014-0235-3

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  • DOI: https://doi.org/10.1007/s13042-014-0235-3

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