Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance

Jingtong Yue, Xin Lin, Zijiu Yang, Chao Ren; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3138-3147

No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. However performance decreases when facing real-world distortion and restored images from restoration models. The reason is that they do not consider the degradation factors of the low-quality images adequately. To address this issue we first introduce the DRI method to obtain degradation vectors and quality vectors of images which separately model the degradation and quality information of low-quality images. After that we add the restoration network to provide the MOS score predictor with degradation information. Then we design the Representation-based Semantic Loss (RS Loss) to assist in enhancing effective interaction between representations. Extensive experimental results demonstrate that the proposed method performs favorably against existing state-of-the-art models on both synthetic and real-world datasets.