Abstract
Humans can detect luminance variations as small as 1% of the background levels. We ask if this sensitivity is determined by the information content in natural images, captured by relationships or correlations between image pixel values. A lack of correlations between the pixel values, e.g., in white noise images, indicates no information content. In experiment 1, we measured human luminance discrimination in natural images. Subjects discriminated between original natural images, I, and their luminance sub-sampled versions (mean luminance matched), Ib, in which pixel values are digitized at pixel depth b=3-9 bits (or 8-512 grey level gradations) per pixel. In experiment 2, we measured human detection of luminance correlation. Subjects discriminated between a white noise image and a residue image Iresidue = I- Ib , i.e., the difference between the original natural image and its luminance sub-sampled version, amplified in luminance/contrast in display to match the noise image. The residue image contains the residue luminance correlation in natural scenes not captured in the sub-sampled image. The percentage correct performances vs. pixel depth b in both experiments matched remarkably well, with 75% correct performance at around 6 bits/pixel and 50% correct at around 7 bits/pixel from both experiments. This strongly suggests that human luminance discrimination sensitivity is optimized to match the information content in natural scenes. We support this hypothesis by showing that the performance curves can be fitted by signal detection theory assuming that the differences in the amount of mutual information between nearby image pixels is responsible for the performances.
