Debiased Mapping for Full-Reference Image Quality Assessment

Abstract

An ideal full-reference image quality (FR-IQA) model should exhibit both high separability for images with different quality and compactness for images with the same or indistinguishable quality. However, existing learning-based FR-IQA models that directly compare images in deep-feature space, usually overly emphasize the quality separability, neglecting to maintain the compactness when images are of similar quality. In our work, we identify that the perception bias mainly stems from an inappropriate subspace where images are projected and compared. For this issue, we propose a Debiased Mapping based quality Measure (DMM), leveraging orthonormal bases formed by singular value decomposition (SVD) in the deep features domain. The SVD effectively decomposes the quality variations into singular values and mapping bases, enabling quality inference with more reliable feature difference measures. Extensive experimental results reveal that our proposed measure could mitigate the perception bias effectively and demonstrates excellent quality prediction performance on various IQA datasets.