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：将图像映射到深度特征空间进行比较的方法已被广泛应用于近年来基于学习的全参考图像质量评估（FR-IQA）模型中。类似于经典的分类任务，用于质量回归的理想映射空间应同时具备类间可分性和类内紧凑性。现有模型高度强调了关注不同质量等级图像判别的类间可分性，然而，维持相同或不可区分质量图像的小目标质量差异的类内紧凑性却未得到研究关注，这可能导致感知偏差的度量。本文揭示，这种偏差主要源于特征投影和比较时所采用的子空间不适当。为解决此问题，我们提出了基于去偏映射的质量度量（DMM），该方法依赖于通过奇异值分解（SVD）形成的深度学习特征的标准正交基。深度学习特征域中的SVD能够利用奇异值和投影基明确分离质量变化，从而通过专门设计的距离度量促进质量推断。在不同IQA数据库上的实验表明，所提出的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我们方法的有效性。该实现将公开提供。