High-dynamic-range scene compression in humans

Single pixel dynamic-range compression alters a particular input value to a unique output value a look-up table. It is used in chemical and most digital photographic systems having S-shaped transforms to render high-range scenes onto low-range media. Postreceptor neural processing is spatial, as shown by the physiological experiments of Dowling, Barlow, Kuffler, and Hubel & Wiesel. Human vision does not render a particular receptor-quanta catch as a unique response. Instead, because of spatial processing, the response to a particular quanta catch can be any color. Visual response is scene dependent. | Stockham proposed an approach to model human range compression using low-spatial frequency filters. Campbell, Ginsberg, Wilson, Watson, Daly and many others have developed spatial-frequency channel models. This paper describes experiments measuring the properties of desirable spatial-frequency filters for a variety of scenes. Given the radiances of each pixel in the scene and the observed appearances of objects in the image, one can calculate the visual mask for that individual image. Here, visual mask is the spatial pattern of changes made by the visual system in processing the input image. It is the spatial signature of human vision. Low-dynamic range images with many white areas need no spatial filtering. High-dynamic-range images with many blacks, or deep shadows, require strong spatial filtering. Sun on the right and shade on the left requires directional filters. These experiments show that variable scenedependent filters are necessary to mimic human vision. Although spatial-frequency filters can model human appearances, the problem still remains that an analysis of the scene is still needed to calculate the scene-dependent strengths of each of the filters for each frequency.