Opinion-Unaware Blind Image Quality Assessment (OU-BIQA) models aim to predict image quality without training on reference images and subjective quality scores. Thereinto, image statistical comparison is a classic paradigm, while the performance is limited by the representation ability of visual descriptors. Deep features as visual descriptors have advanced IQA in recent research, but they are discovered to be highly texture-biased and lack of shape-bias. On this basis, we find out that image shape and texture cues respond differently towards distortions, and the absence of either one results in an incomplete image representation. Therefore, to formulate a well-round statistical description for images, we utilize the shapebiased and texture-biased deep features produced by Deep Neural Networks (DNNs) simultaneously. More specifically, we design a Shape-Texture Adaptive Fusion (STAF) module to merge shape and texture information, based on which we formulate qualityrelevant image statistics. The perceptual quality is quantified by the variant Mahalanobis Distance between the inner and outer Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images. The proposed DSTS delicately utilizes shape-texture statistical relations between different data scales in the deep domain, and achieves state-of-the-art (SOTA) quality prediction performance on images with artificial and authentic distortions.

翻译：无意见盲图像质量评估（Opinion-Unaware Blind Image Quality Assessment, OU-BIQA）模型旨在无需参考图像和主观质量分数训练的条件下预测图像质量。其中，图像统计比较是经典范式，但其性能受限于视觉描述符的表征能力。近年来，深度特征作为视觉描述符推动了图像质量评估（IQA）的发展，但研究发现深度特征具有高度纹理偏差且缺乏形状偏差。基于此，我们发现图像形状与纹理线索对失真表现出不同响应，缺失其中任一元素均会导致图像表征不完整。因此，为构建图像的全方位统计描述，我们同时利用深度神经网络（DNNs）产生的具有形状偏差和纹理偏差的深度特征。具体而言，我们设计了一个形状-纹理自适应融合（Shape-Texture Adaptive Fusion, STAF）模块以合并形状和纹理信息，并基于此构建与质量相关的图像统计量。感知质量通过内部与外部形状-纹理统计量（DSTS）之间的变体马氏距离（Mahalanobis Distance）量化，其中内部与外部统计量分别描述失真图像与自然图像的质量指纹。所提出的DSTS方法巧妙利用了深度域中不同数据尺度间的形状-纹理统计关系，在包含人工失真和自然失真的图像上实现了最先进（SOTA）的质量预测性能。