Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance
This work addresses image quality assessment for distorted images, which is an incremental improvement for applications in image processing and computer vision.
The paper tackled the challenge of No-Reference Image Quality Assessment for distorted images by introducing a method that models degradation and quality information separately, with restoration assistance, achieving favorable performance against state-of-the-art models on synthetic and real-world datasets.
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.