Efficient image representations and features

Michael Dorr; Eleonora Vig; Erhardt Barth

doi:10.1117/12.2002300

14 March 2013 Efficient image representations and features

Michael Dorr, Eleonora Vig, Erhardt Barth

Proceedings Volume 8651, Human Vision and Electronic Imaging XVIII; 86510R (2013) https://doi.org/10.1117/12.2002300
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Interdisciplinary research in human vision and electronic imaging has greatly contributed to the current state of the art in imaging technologies. Image compression and image quality are prominent examples and the progress made in these areas relies on a better understanding of what natural images are and how they are perceived by the human visual system. A key research question has been: given the (statistical) properties of natural images, what are the most efficient and perceptually relevant image representations, what are the most prominent and descriptive features of images and videos? We give an overview of how these topics have evolved over the 25 years of HVEI conferences and how they have influenced the current state of the art. There are a number of striking parallels between human vision and electronic imaging. The retina does lateral inhibition, one of the early coders was using a Laplacian pyramid; primary visual cortical areas have orientation- and frequency-selective neurons, the current JPEG standard defines similar wavelet transforms; the brain uses a sparse code, engineers are currently excited about sparse coding and compressed sensing. Some of this has indeed happened at the HVEI conferences and we would like to distill that.

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Michael Dorr, Eleonora Vig, and Erhardt Barth "Efficient image representations and features", Proc. SPIE 8651, Human Vision and Electronic Imaging XVIII, 86510R (14 March 2013); https://doi.org/10.1117/12.2002300

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KEYWORDS

Image compression

Image quality

Transform theory

Independent component analysis

Visualization

Image transmission

Principal component analysis

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