Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment

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Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment
Title:
Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment
Journal Title:
IEEE Transactions on Image Processing
Keywords:
Publication Date:
24 April 2024
Citation:
Shen, W., Zhou, M., Luo, J., Li, Z., & Kwong, S. (2024). Graph-Represented Distribution Similarity Index for Full-Reference Image Quality Assessment. IEEE Transactions on Image Processing, 33, 3075–3089. https://doi.org/10.1109/tip.2024.3390565
Abstract:
In this paper, we propose a graph-represented image distribution similarity (GRIDS) index for full-reference (FR) image quality assessment (IQA), which can measure the perceptual distance between distorted and reference images by assessing the disparities between their distribution patterns under a graph based representation. First, we transform the input image into a graph-based representation, which is proven to be a versatile and effective choice for capturing visual perception features. This is achieved through the automatic generation of a vision graph from the given image content, leading to holistic perceptual associations for irregular image regions. Second, to reflect the perceived image distribution, we decompose the undirected graph into cliques and then calculate the product of the potential functions for the cliques to obtain the joint probability distribution of the undirected graph. Finally, we compare the distances between the graph feature distributions of the distorted and reference images at different stages; thus, we combine the distortion distribution measurements derived from different graph model depths to determine the perceived quality of the distorted images. The empirical results obtained from an extensive array of experiments underscore the competitive nature of our proposed method, which achieves performance on par with that of the state-of-the-art methods, demonstrating its exceptional predictive accuracy and ability to maintain consistent and monotonic behaviour in image quality prediction tasks. The source code is publicly available at the following website.
License type:
Publisher Copyright
Funding Info:
This work was supported in part by the National Natural Science Foundation of China under Grant 62176027; in part by the Key Projects of Basic Strengthening Plan under Grant 2022-JCJQ-ZD-018-11; in part by the Chongqing Talent under Grant cstc2024ycjh-bgzxm0082; in part by the Joint Equipment Pre Research and Key Fund Project of the Ministry of Education under Grant 8091B012207; in part by the Human Resources and Social Security Bureau Project of Chongqing under Grant cx2020073; in part by the Guangdong Oppo Mobile Telecommunications Corporation Ltd., under Grant H20221694; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11209819 and Grant CityU 9042816; in part by the Hong Kong GRF-RGC General Research Fund under Grant 11203820 and Grant CityU 9042598.
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1941-0042
1057-7149
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