ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. Such a remarkable byproduct has often been identified as an emergent property in previous studies. In this work, we attribute such capability to the intrinsic texture-sensitive characteristic that classifies images using texture features. We fully exploit this characteristic to develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. Specifically, we compute the distance correlation, a highly promising yet relatively under-investigated statistic, between reference and distorted images in the deep feature domain. In addition, the distance correlation quantifies both linear and nonlinear feature relationships, which is far beyond the widely used first-order and second-order statistics in the feature space. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets, one perceptual similarity dataset, two texture similarity datasets, and one geometric transformation dataset. Moreover, we optimize the proposed model to generate a broad spectrum of texture patterns, by treating the model as the style loss function for neural style transfer (NST). Extensive experiments demonstrate that the proposed texture synthesis and NST methods achieve the best quantitative and qualitative results. We release our code at https://github.com/h4nwei/DeepDC.

翻译：ImageNet预训练的深度神经网络在构建有效图像质量评估模型方面展现出显著的可迁移性。这种引人注目的副产品在以往研究中常被视为新兴属性。本研究将这种能力归因于利用纹理特征进行图像分类的内在纹理敏感特性，并充分挖掘该特性开发了一种完全基于预训练深度神经网络特征的全参考图像质量评估模型。具体而言，我们在深度特征域中计算参考图像与失真图像之间的距离相关性——这是一种极具潜力但尚未被充分研究的统计量。此外，距离相关性可同时量化线性和非线性特征关系，其能力远超特征空间中广泛使用的一阶与二阶统计量。我们通过五个标准图像质量评估数据集、一个感知相似度数据集、两个纹理相似度数据集以及一个几何变换数据集上的综合实验，证明了所提质量模型的优越性。进一步地，我们将该模型作为神经风格迁移的损失函数，通过优化生成多样化的纹理模式。大量实验表明，所提出的纹理合成与神经风格迁移方法在定量与定性评估中均达到最优结果。相关代码已开源至https://github.com/h4nwei/DeepDC。