论文标题
DEEPWSD：在感知空间中的降解到深度特征空间中的瓦斯汀距离
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
论文作者
论文摘要
现有的基于深度学习的全参考IQA（FR-IQA）模型通常会通过明确比较特征，以确定性的方式预测图像质量，从而通过相应的特征与参考图像的空间相对远，从而衡量了图像的严重扭曲程度。本文中，我们从不同的角度看这个问题，并提议从统计分布的角度对知觉空间中的质量降解进行建模。因此，质量是根据深度特征域中的Wasserstein距离来测量的。更具体地说，根据执行最终质量评分，测量了预训练VGG网络的每个阶段的1DWASSERSTEIN距离。 Deep Wasserstein距离（DEEPWSD）在神经网络的特征上执行的，可以更好地解释由各种扭曲引起的质量污染，并提出了先进的质量预测能力。广泛的实验和理论分析表明，在质量预测和优化方面，提出的DEEPWSD的优越性。
Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images. Herein, we look at this problem from a different viewpoint and propose to model the quality degradation in perceptual space from a statistical distribution perspective. As such, the quality is measured based upon the Wasserstein distance in the deep feature domain. More specifically, the 1DWasserstein distance at each stage of the pre-trained VGG network is measured, based on which the final quality score is performed. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination caused by various types of distortions and presents an advanced quality prediction capability. Extensive experiments and theoretical analysis show the superiority of the proposed DeepWSD in terms of both quality prediction and optimization.
