Multidimensional image retargeting

  • Authors:
  • Francesco Banterle;Alessandro Artusi;Tunç O. Aydin;Piotr Didyk;Elmar Eisemann;Diego Gutierrez;Rafal Mantiuk;Karol Myszkowski

  • Affiliations:
  • -;-;-;-;-;-;-;-

  • Venue:
  • SIGGRAPH Asia 2011 Courses
  • Year:
  • 2011

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Abstract

Retargeting refers to the process by which an image or video is adapted from the display device for which it was meant (target display) to another one (retarget display). The retarget display has different features from the target one such as dynamic range, discretization levels, color gamut, multi-view, and refresh rate spatial resolution. This is a very relevant topic in graphics, given the increasing number of display devices from large, high-contrast screens to small cell phones with limited dynamic range; a lot of techniques are being published in different venues, and it's hard to keep up. For most cases retargeting can be an ill-posed problem, for example in the process of displaying Low Dynamic Range (LDR) or 8-bit content on High Dynamic Range (HDR) displays. Such a problem requires the retargeting algorithm to generate new content which is missing in the input image/frame. In this course, we will present the latest solutions and techniques for retargeting images along various dimensions such as dynamic range, colors, temporal and spatial resolutions, and for the first time offer a much-needed holistic view of the field. Moreover, we are going to show how to measure and analyze the changes applied to an image or video in terms of quality using both psychophysical experiments (subjective) and computational metrics (objective). The course should be of interest to anyone involved in graphics in a broader sense, given the almost unavoidable need to retarget results to different devices -from developers interested in implementing retargeting techniques, to users that just need an overall perspective. For researchers fully engaged in developing multi-dimensional retargeting techniques, this course will serve as a solid background for future algorithms.