Chen, F.; Fu, H.; Yu, H.; Chu, Y. Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality. Sensors2023, 23, 4974.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality. Sensors 2023, 23, 4974.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality. Sensors2023, 23, 4974.
Chen, F.; Fu, H.; Yu, H.; Chu, Y. Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality. Sensors 2023, 23, 4974.
Abstract
Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
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