Vision Models for High Dynamic Range and Wide Colour Gamut Imaging: Techniques and Applications

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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.

Author(s): Marcelo Bertalmío
Series: Computer Vision and Pattern Recognition
Edition: 1
Publisher: Academic Press
Year: 2019

Language: English
Pages: 315

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