In the 1950s Horace Barlow and Fred Attneave suggested a connection between sensory systems and how they are adapted to the environment: early vision evolved to maximise the information it conveys about incoming signals. Following Shannon's definition, this information was described using the probability of the images taken from natural scenes. Previously, direct accurate predictions of image probabilities were not possible due to computational limitations. Despite the exploration of this idea being indirect, mainly based on oversimplified models of the image density or on system design methods, these methods had success in reproducing a wide range of physiological and psychophysical phenomena. In this paper, we directly evaluate the probability of natural images and analyse how it may determine perceptual sensitivity. We employ image quality metrics that correlate well with human opinion as a surrogate of human vision, and an advanced generative model to directly estimate the probability. Specifically, we analyse how the sensitivity of full-reference image quality metrics can be predicted from quantities derived directly from the probability distribution of natural images. First, we compute the mutual information between a wide range of probability surrogates and the sensitivity of the metrics and find that the most influential factor is the probability of the noisy image. Then we explore how these probability surrogates can be combined using a simple model to predict the metric sensitivity, giving an upper bound for the correlation of 0.85 between the model predictions and the actual perceptual sensitivity. Finally, we explore how to combine the probability surrogates using simple expressions, and obtain two functional forms (using one or two surrogates) that can be used to predict the sensitivity of the human visual system given a particular pair of images.

翻译：1950年代，霍勒斯·巴洛和弗雷德·阿特尼夫提出感官系统及其适应环境之间存在关联：早期视觉进化以最大化传递的输入信号信息。遵循香农的定义，这种信息通过自然场景中图像的概率进行描述。此前，由于计算能力的限制，无法直接准确预测图像概率。尽管这一思想的探索较为间接，主要基于过度简化的图像密度模型或系统设计方法，但这些方法在复现广泛的生理和心理物理现象方面取得了成功。本文直接评估自然图像的概率，并分析其如何决定感知灵敏度。我们采用与人类主观评价高度相关的图像质量指标作为人类视觉的替代指标，并利用先进的生成模型直接估计概率。具体而言，我们分析全参考图像质量指标的灵敏度如何通过直接从自然图像概率分布导出的量来预测。首先，我们计算多种概率替代指标与指标灵敏度之间的互信息，发现噪声图像的概率是最具影响力的因素。接着，我们探讨如何通过简单模型组合这些概率替代指标来预测指标灵敏度，模型预测与实际感知灵敏度之间的相关性上限达到0.85。最后，我们研究如何通过简单表达式组合概率替代指标，得到两种函数形式（使用一个或两个替代指标），可用于预测给定特定图像对时人类视觉系统的灵敏度。