Optimized Perceptual Tone Mapping for Contrast Enhancement of Images

Existing contrast enhancement techniques, such as histogram equalization (HE) and optimal contrast-tone mapping (OCTM), construct a pixel mapping function based on probability. However, they often allocate a large dynamic range to smooth areas, thus preventing the allocation of dynamic range resources in some regions that people are more interested in. To deal with this problem, we propose optimized perceptual tone mapping (OPTM), which performs contrast enhancement of images considering human visual attention. First, we construct a saliency histogram based on human visual attention to quantitatively measure the degree of visual attention instead of a probability histogram. Then, we perform contrast enhancement of images subject to constraints, such as the maximum tone distortion allowed. Finally, we adjust a pixel mapping function based on a just-noticeable-distortion model to prevent overenhancement. The experimental results demonstrate that OPTM produces visually pleasing results without overenhancement in large smooth regions and achieves a significant performance improvement in contrast enhancement.