Chen, F.; Fu, H.; Yu, H.; Chu, Y. No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network. Appl. Sci.2023, 13, 6802.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network. Appl. Sci. 2023, 13, 6802.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network. Appl. Sci.2023, 13, 6802.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network. Appl. Sci. 2023, 13, 6802.
Abstract
When the image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information of the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. To enable the model to focus on both global and local features, a multi-scale feature fusion module is proposed. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
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