Vision Models for High Dynamic Range and Wide Colour Gamut Imaging

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. Introduction 2. The biological basis of vision: the retina 3. The biological basis ov vision: LGN, visual cortex and L+NL models 4. Adaptation and efficient coding 5. Brightness perception and encoding curves 6. Colour representation and colour gamuts 7. Histogram equalisation and vision models 8. Vision models for gamut mapping in cinema 9. Vision models for tone mapping in cinema 10. Extensions and applications 11. Open problems: an argument for new vision models rather than new algorithms