Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

To enhance the overall viewing experience (for cinema, TV, games, AR/VR) the media industry is continuously striving to improve image quality. Currently the emphasis is on High Dynamic Range (HDR) and Wide Colour Gamut (WCG) technologies, which yield images with greater contrast and more vivid colours. The uptake of these technologies, however, has been hampered by the significant challenge of understanding the science behind visual perception. Vision Models for High Dynamic Range and Wide Colour Gamut Imaging provides university researchers and graduate students in computer science, computer engineering, vision science, as well as industry R&D engineers, an insight into the science and methods for HDR and WCG. It presents the underlying principles and latest practical methods in a detailed and accessible way, highlighting how the use of vision models is a key element of all state-of-the-art methods for these emerging technologies.
- Presents the underlying vision science principles and models that are essential to the emerging technologies of HDR and WCG- Explores state-of-the-art techniques for tone and gamut mapping- Discusses open challenges and future directions of HDR and WCG research

Marcelo Bertalmío (Montevideo, 1972) is a full professor at Universitat Pompeu Fabra, Spain, in the Information and Communication Technologies Department. He received B.Sc. and M.Sc. degrees in electrical engineering from the Universidad de la República, Uruguay, and a Ph.D. degree in electrical and computer engineering from the University of Minnesota in 2001. He was awarded the 2012 SIAG/IS Prize of the Society for Industrial and Applied Mathematics (SIAM) for co-authoring the most relevant image processing work published in the period 2008-2012. Has received the Femlab Prize, the Siemens Best Paper Award, the Ramón y Cajal Fellowship, and the ICREA Academia Award, among other honours. He was Associate Editor for SIAM-SIIMS and elected secretary of SIAM's activity group on imaging. He has obtained an ERC Starting Grant for his project "Image processing for enhanced cinematography and two ERC Proof of Concept Grants to bring to market tone mapping and gamut mapping technologies. He is co-coordinator of two H2020 projects, HDR4EU and SAUCE, involving world-leading companies in the film industry. He has written a book titled "Image Processing for Cinema, published by CRC Press in 2014, and edited the book "Denoising of Photographic Images and Video published by Springer in 2018. His current research interests are in developing image processing algorithms for cinema that mimic neural and perceptual processes in the visual system, and in investigating new vision models based on efficient representation, with fine-tuning by movie professionals.

1. Introduction2. The biological basis of vision: the retina3. The biological basis ov vision: LGN, visual cortex and L+NL models4. Adaptation and efficient coding5. Brightness perception and encoding curves6. Colour representation and colour gamuts7. Histogram equalisation and vision models8. Vision models for gamut mapping in cinema9. Vision models for tone mapping in cinema10. Extensions and applications11. Open problems: an argument for new vision models rather than new algorithms