As new displays and cameras offer enhanced color capabilities, thereis a need to extend the precision of digital content. High DynamicRange (HDR) imaging encodes images and video with higher than normalbit-depth precision, enabling representation of the complete colorgamut and the full visible range of luminance.This thesis addresses three problems of HDR imaging: the measurementof visible distortions in HDR images, lossy compression for HDR video,and artifact-free image processing. To measure distortions in HDRimages, we develop a visual difference predictor for HDR images thatis based on a computational model of the human visual system. Toaddress the problem of HDR image encoding and compression, we derive aperceptually motivated color space for HDR pixels that can efficientlyencode all perceivable colors and distinguishable shades ofbrightness. We use the derived color space to extend the MPEG-4 videocompression standard for encoding HDR movie sequences. We also proposea backward-compatible HDR MPEG compression algorithm that encodes botha low-dynamic range and an HDR video sequence into a single MPEGstream. Finally, we propose a framework for image processing in thecontrast domain. The framework transforms an image intomulti-resolution physical contrast images (maps), which are thenrescaled in just-noticeable-difference (JND) units. The application ofthe framework is demonstrated with a contrast-enhancing tone mappingand a color to gray conversion that preserves color saliency.