Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning

• URL: http://arxiv.org/abs/2312.06995v1
• Date: Tue, 12 Dec 2023 06:01:41 GMT
• Title: Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning
• Authors: Jinsong Shi, Pan Gao, Jie Qin
• Abstract summary: We propose a novel Contrastive Learning (SCL) and Transformer-based NR-IQA model SaTQA. We first train a model on a large-scale synthetic dataset by SCL to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Experimental results on seven standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets
• Score: 36.695247860715874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited capability when facing unknown authentic distortion datasets. To further improve NR-IQA performance, in this paper, a novel supervised contrastive learning (SCL) and Transformer-based NR-IQA model SaTQA is proposed. We first train a model on a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Finally, we propose the Patch Attention Block (PAB) to obtain the final distorted image quality score by fusing the degradation features learned from contrastive learning with the perceptual distortion information extracted by the backbone network. Experimental results on seven standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets. Code is available at https://github.com/I2-Multimedia-Lab/SaTQA

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