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提出生物视觉与信息最大化之间存在关联的假说。遵循Shannon的理论，信息被定义为使用自然图像的概率。自那以后，许多生理学和心理物理学现象都是从信息最大化、高效编码或最优去噪等原理推导出来的。然而，目前尚不清楚这种联系如何从图像概率的数学表达中体现出来。首先，经典推导过程对概率模型和传感器行为施加了严格假设。此外，由于传统图像模型无法提供准确的概率估计，直接验证这一假说受到限制。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，并分析如何将概率相关因素组合起来，通过最先进的主观图像质量指标的灵敏度来预测人类感知。我们使用信息论和回归分析，仅需两个概率相关因素组合即可达到与主观指标0.8的相关性。这种基于概率的灵敏度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得到了心理物理学验证。