Robust Content-Variant Reference Image Quality Assessment Via Similar Patch Matching

Abstract

Although image quality assessment (IQA) methods have achieved remarkable success in the past decades, full-reference IQA is limited to reference images, while no-reference IQA has relatively poor performance. To boost the performance of IQA models in the no-reference scenario, a new class of IQA methods using content-variant high-quality images as references have emerged. However, the existing approaches do not take advantage of the content information of the content-variant reference (CVR) images, resulting in the insufficient use of high-quality reference information and the unsatisfactory robustness of the algorithm performance. To effectively utilize CVR images and make the algorithm more robust, we propose a CVR IQA scheme based on similar patch matching. For each image patch to be evaluated, the patch with the most similar content is first searched in the CVR image as the reference patch. Since the two patches are more similar, more useful reference information can be extracted. A similarity calculation module based on cross-attention is designed to find content-similar patches. Extensive experimental results show that the proposed algorithm has good performance and robustness.