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.

翻译：将图像映射至深度特征空间进行比较的方法，在近期基于学习的全参考图像质量评估（FR-IQA）模型中得到广泛应用。类似于经典分类任务，质量回归的理想映射空间需同时具备类间可分性与类内紧凑性。现有模型高度强调区分不同质量等级图像的类间可分性，但对于维持相同或不可区分质量图像间客观质量方差较小的类内紧凑性缺乏关注，这可能导致感知偏向性度量。本文揭示，此类偏差主要由特征投影与比较时所处的不合适子空间引发。为此，我们提出基于去偏映射的质量度量方法（DMM），该方法依托奇异值分解（SVD）形成的深度学习特征标准正交基。深度学习特征域中的SVD，通过奇异值与投影基向量对质量变化进行显著分离，从而借助专门设计距离度量实现质量推断。在不同IQA数据库上的实验表明，该映射方法能有效缓解感知偏差，且在质量预测上的优越性能验证了本方法的有效性。相关实现将公开发布。