A population-based approach to point-sampling spatial color algorithms

Inspired by the behavior of the human visual system, spatial color algorithms perform image enhancement by correcting the pixel channel lightness based on the spatial distribution of the intensities in the surrounding area. The two visual contrast enhancement algorithms RSR and STRESS belong to this family of models: they rescale the input based on local reference values, which are determined by exploring the image by means of random point samples, called sprays. Due to the use of sampling, they may yield a noisy output. In this paper, we introduce a probabilistic formulation of the two models: our algorithms (RSR-P and STRESS-P) rely implicitly on the whole population of possible sprays. For processing larger images, we also provide two approximated algorithms that exploit a suitable target-dependent space quantization. Those spray population-based formulations outperform RSR and STRESS in terms of the processing time required for the production of noiseless outputs. We argue that this population-based approach, which can be extended to other members of the family, complements the sampling-based approach, in that it offers not only a better control in the design of approximated algorithms, but also additional insight into individual models and their relationships. We illustrate the latter point by providing a model of halo artifact formation.