A Psychovisual Analysis on Deep CNN Features for Perceptual Metrics and A Novel Psychovisual Loss
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The efficacy of Deep Convolutional Neural Network (CNN) features as perceptual metrics has been demonstrated by researchers. Nevertheless, any thorough analysis in the context of human visual perception on 'why deep CNN features perform well as perceptual metrics?', 'Which layers are better?', 'Which feature maps are better?' and most importantly, 'Why they are better?' has not been studied. In this paper, we address these issues and provide an analysis for deep CNN features in terms of Human Visual System (HVS) characteristics. We scrutinize the frequency tuning of feature maps in a trained deep CNN (e.g., VGG-16) by applying grating stimuli of different spatial frequencies as input, presenting a novel analytical technique that may help us to better understand and compare characteristics of CNNs with the human brain. We observe that feature maps behave as spatial frequency-selective filters which can be best explained by the well-established 'spatial frequency theory' for the visual cortex. We analyze the frequency sensitivity of deep features in relation to the human contrast sensitivity function. Based on this, we design a novel Visual Frequency Sensitivity Score (VFSS) to explain and quantify the efficacy of feature maps as perceptual metrics. Based on our psychovisual analysis, we propose a weighting mechanism to discriminate between feature maps on the basis of their perceptual properties and use this weighting to improve the VGG perceptual loss. The proposed psychovisual loss results in reconstructions with less distortion and better perceptive visual quality.
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