–high-fidelity Imaging– the Computational Models of the Human Visual System in High Dynamic Range Video Compression, Visible Difference Prediction and Image Processing Dissertation

As new displays and cameras offer enhanced color capabilities, there is a need to extend the precision of digital content. High Dynamic Range (HDR) imaging encodes images and video with higher than normal bit-depth precision, enabling representation of the complete color gamut and the full visible range of luminance. This thesis addresses three problems of HDR imaging: the measurement of visible distortions in HDR images, lossy compression for HDR video, and artifact-free image processing. To measure distortions in HDR images, we develop a visual difference predictor for HDR images that is based on a computational model of the human visual system. To address the problem of HDR image encoding and compression, we derive a perceptually motivated color space for HDR pixels that can efficiently encode all perceivable colors and distinguishable shades of brightness. We use the derived color space to extend the MPEG-4 video compression standard for encoding HDR movie sequences. We also propose a backward-compatible HDR MPEG compression algorithm that encodes both a low-dynamic range and an HDR video sequence into a single MPEG stream. Finally, we propose a framework for image processing in the contrast domain. The framework transforms an image into multi-resolution physical contrast images (maps), which are then rescaled in just-noticeable-difference (JND) units. The application of the framework is demonstrated with a contrast-enhancing tone mapping and a color to gray conversion that preserves color saliency.