Overview and Evaluation of the JPEG XT HDR Image Compression Standard

Standards play an important role in providing a common set of specifications and allowing inter-operability between devices and systems. Until recently, no standard for High Dynamic Range (HDR) image coding had been adopted by the market, and HDR imaging relies on proprietary and vendor specific formats which are unsuitable for storage or exchange of such images. To resolve this situation, the JPEG Committee is developing a new coding standard called JPEG~XT that is backwards compatible to the popular JPEG compression, allowing it to be implemented using standard 8-bit JPEG coding hardware or software. In this paper, we present design principles and technical details of JPEG~XT. It is based on a two-layers design, a base layer containing a Low Dynamic Range (LDR) image accessible to legacy implementations, and an extension layer providing the full dynamic range. The paper introduces three of currently defined profiles in JPEG~XT, each constraining the common decoder architecture to a subset of allowable configurations. We assess the coding efficiency of each profile extensively through subjective assessments, using 24 naive subjects to evaluate 20 images, and objective evaluations, using 106 images with five different tone-mapping operators and at 100 different bit rates. The objective results (based on benchmarking with subjective scores) demonstrate that JPEG~XT can encode HDR images at bit rates varying from 1.1 to 1.9 bit/pixel for estimated mean opinion score (MOS) values above 4.5 out of 5, which is considered as fully transparent in many applications. This corresponds to 23-times bitstream reduction compared to lossless OpenEXR PIZ compression

High-fidelity imaging : the computational models of the human visual system in high dynamic range video compression, visible difference prediction and image processing

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.Aktuelle Innovationen in der Farbverarbeitung bei Bildschirmen und Kameras erzwingen eine Präzisionserweiterung bei digitalen Medien. High Dynamic Range (HDR) kodieren Bilder und Video mit einer grösseren Bittiefe pro Pixel, und ermöglichen damit die Darstellung des kompletten Farbraums und aller sichtbaren Helligkeitswerte. Diese Arbeit konzentriert sich auf drei Probleme in der HDR-Verarbeitung: Messung von für den Menschen störenden Fehlern in HDR-Bildern, verlustbehaftete Kompression von HDR-Video, und visuell verlustfreie HDR-Bildverarbeitung. Die Messung von HDR-Bildfehlern geschieht mittels einer Vorhersage von sichtbaren Unterschieden zweier HDR-Bilder. Die Vorhersage basiert dabei auf einer Modellierung der menschlichen Sehens. Wir addressieren die Kompression und Kodierung von HDR-Bildern mit der Ableitung eines perzeptuellen Farbraums für HDR-Pixel, der alle wahrnehmbaren Farben und deren unterscheidbaren Helligkeitsnuancen effizient abbildet. Danach verwenden wir diesen Farbraum für die Erweiterung des MPEG-4 Videokompressionsstandards, welcher sich hinfort auch für die Kodierung von HDR-Videosequenzen eignet. Wir unterbreiten weiters eine rückwärts-kompatible MPEG-Kompression von HDR-Material, welche die übliche YUV-Bildsequenz zusammen mit dessen HDRVersion in einen gemeinsamen MPEG-Strom bettet. Abschliessend erklären wir unser Framework zur Bildverarbeitung in der Kontrastdomäne. Das Framework transformiert Bilder in mehrere physikalische Kontrastauflösungen, um sie danach in Einheiten von just-noticeable-difference (JND, noch erkennbarem Unterschied) zu reskalieren. Wir demonstrieren den Nutzen dieses Frameworks anhand von einem kontrastverstärkenden Tone Mapping-Verfahren und einer Graukonvertierung, die die urspr ünglichen Farbkontraste bestmöglich beibehält

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency