In the 1950s, Barlow and Attneave hypothesised a link between biological vision and information maximisation. Following Shannon, information was defined using the probability of natural images. A number of physiological and psychophysical phenomena have been derived ever since from principles like info-max, efficient coding, or optimal denoising. However, it remains unclear how this link is expressed in mathematical terms from image probability. First, classical derivations were subjected to strong assumptions on the probability models and on the behaviour of the sensors. Moreover, the direct evaluation of the hypothesis was limited by the inability of the classical image models to deliver accurate estimates of the probability. In this work we directly evaluate image probabilities using an advanced generative model for natural images, and we analyse how probability-related factors can be combined to predict human perception via sensitivity of state-of-the-art subjective image quality metrics. We use information theory and regression analysis to find a combination of just two probability-related factors that achieves 0.8 correlation with subjective metrics. This probability-based sensitivity is psychophysically validated by reproducing the basic trends of the Contrast Sensitivity Function, its suprathreshold variation, and trends of the Weber-law and masking.

翻译：在20世纪50年代，Barlow和Attneave提出了生物视觉与信息最大化之间存在联系的假说。遵循香农的理论，信息被定义为自然图像的概率。此后，许多生理和心理物理现象都源于信息最大化、高效编码或最优去噪等原理。然而，这一联系如何从图像概率的数学表达中得以体现仍不明确。首先，经典推导方法对概率模型和传感器行为施加了严格的假设。此外，由于传统图像模型无法准确估计概率值，对该假说的直接评估受到限制。在本研究中，我们利用先进的自然图像生成模型直接评估图像概率，并通过分析概率相关因素如何结合来预测人类感知（基于最先进的主观图像质量度量的敏感度）。我们运用信息论和回归分析发现，仅需两个概率相关因素的组合即可实现与主观度量0.8的相关性。通过复现对比敏感度函数的基本趋势、其超阈值变化以及韦伯定律和掩蔽效应的趋势，我们对这种基于概率的敏感度进行了心理物理学验证。