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11 July 2024 Double stream lightweight network NR-IQA framework based on gradient map
Meizhuo Xin, Kuang Wang, Xiaoli Jiang
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Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100C (2024) https://doi.org/10.1117/12.3034802
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China
Abstract
In order to make the lightweight network more effective to understand the feature representation of no-reference image quality assessment (NR-IQA). We propose a dual-stream SqueezeNet combined with gradient maps. SqueezeNet can improve the accuracy of the training model while reducing the number of parameters. The dual-stream includes two input channels: RGB image patches and gradient images. RGB image stream pays more attention to the intensity of image information, and gradient stream focuses on extracting detailed image feature information in detail. We use SqueezeNet to train image patches and gradient images separately, and finally obtain the final image quality score by averaging the scores of the patches. We conducted extensive experiments on the basic data sets LIVE, TID2013, and CSIQ. In the experiment, our proposed approach achieves an SROCC score of 0.895 with only 0.42 M, in the WN distortion type of LIVE, the highest SROCC can reach 0.991, the JPEG distortion type of CSIQ can reach 0.966. The cross-data set verification experiment proves that the proposed model has high robustness.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Meizhuo Xin, Kuang Wang, and Xiaoli Jiang "Double stream lightweight network NR-IQA framework based on gradient map", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100C (11 July 2024); https://doi.org/10.1117/12.3034802
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KEYWORDS
Image quality
RGB color model
Distortion
Education and training
Data modeling
Image processing
Feature extraction
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