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SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment

Sun, W; Liao, Q; Xue, JH; Zhou, F; (2018) SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment. IEEE Transactions on Image Processing , 27 (9) pp. 4232-4244. 10.1109/TIP.2018.2837341. Green open access

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Abstract

Full-reference image quality assessment algorithms usually perform comparisons of features extracted from square patches. These patches do not have any visual meanings. On the contrary, a superpixel is a set of image pixels that share similar visual characteristics and is thus perceptually meaningful. Features from superpixels may improve the performance of image quality assessment. Inspired by this, we propose a new superpixel-based similarity index by extracting perceptually meaningful features and revising similarity measures. The proposed method evaluates image quality on the basis of three measurements, namely, superpixel luminance similarity, superpixel chrominance similarity, and pixel gradient similarity. The first two measurements assess the overall visual impression on local images. The third measurement quantifies structural variations. The impact of superpixel-based regional gradient consistency on image quality is also analyzed. Distorted images showing high regional gradient consistency with the corresponding reference images are visually appreciated. Therefore, the three measurements are further revised by incorporating the regional gradient consistency into their computations. A weighting function that indicates superpixel-based texture complexity is utilized in the pooling stage to obtain the final quality score. Experiments on several benchmark databases demonstrate that the proposed method is competitive with the state-of-the-art metrics.

Type: Article
Title: SPSIM: A Superpixel-Based Similarity Index for Full-Reference Image Quality Assessment
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TIP.2018.2837341
Publisher version: https://doi.org/10.1109/TIP.2018.2837341
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Feature extraction, Image color analysis, Image quality, Visualization, Indexes, Image segmentation, Distortion measurement
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10050961
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