Debiased Mapping for Full-Reference Image Quality Assessment

Abstract

Mapping images to deep feature space for comparisons has been wildly adopted in recent learning-based full-reference image quality assessment (FR-IQA) models. Analogous to the classical classification task, the ideal mapping space for quality regression should possess both inter-class separability and intra-class compactness. The inter-class separability that focuses on the discrimination of images with different quality levels has been highly emphasized in existing models. However, the intra-class compactness that maintains small objective quality variance of images with the same or indistinguishable quality escapes the research attention, potentially leading to the perception-biased measures. In this paper, we reveal that such bias is mainly caused by the unsuitable subspace that the features are projected and compared in. To account for this, we develop the Debiased Mapping based quality Measure (DMM), which relies on the orthonormal bases of deep learning features formed by singular value decomposition (SVD). The SVD in deep learning feature domain, which overwhelmingly separates the quality variations with singular values and projection bases, facilitates the quality inference with dedicatedly designed distance measure. Experiments on different IQA databases demonstrate the mapping method is able to mitigate the perception bias efficiently, and the superior performance on quality prediction verifies the effectiveness of our method. The implementation will be publicly available.