DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space

• URL: http://arxiv.org/abs/2208.03323v1
• Date: Fri, 5 Aug 2022 02:46:12 GMT
• Title: DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
• Authors: Xigran Liao, Baoliang Chen, Hanwei Zhu, Shiqi Wang, Mingliang Zhou, Sam Kwong
• Abstract summary: We propose to model the quality degradation in perceptual space from a statistical distribution perspective. The quality is measured based upon the Wasserstein distance in the deep feature domain. The deep Wasserstein distance (DeepWSD) performed on features from neural networks enjoys better interpretability of the quality contamination.
• Score: 67.07476542850566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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