Image sequence coding using spatial/spatial-frequency representations

Todd Randall Reed

doi:10.1117/12.135969

27 August 1992 Image sequence coding using spatial/spatial-frequency representations

Todd Randall Reed

Proceedings Volume 1666, Human Vision, Visual Processing, and Digital Display III; (1992) https://doi.org/10.1117/12.135969
Event: SPIE/IS&T 1992 Symposium on Electronic Imaging: Science and Technology, 1992, San Jose, CA, United States

Abstract

Spatial/spatial-frequency representations have proven to be an interesting and powerful framework for the simulation of a number of visual effects. Results consistent with observations of the human visual system have been obtained at levels ranging from the shape of receptive field profiles to perceptual grouping and texture segmentation. A number of representations are under study, in a number of different fields. A key issue in comparing these representations is the resolution that can be attained (simultaneously) in the joint domain. The uncertainty principle dictates that arbitrarily high resolution cannot be achieved in both space and spatial-frequency. Joint resolution can range from singular functions in space (with infinite extent in spatial-frequency), to the reverse (e.g., the pixel representation at one extreme, and the Fourier transform at the other). In this paper, we discuss some of the available representations in the context of image sequence coding, and establish some of the characteristics desired in a representation for this application. We show that the joint resolution of a representation, in particular, can affect the performance of coding methods based on that representation. Examples which illustrate this point using industry standard DCT-based methods are given.

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Todd Randall Reed "Image sequence coding using spatial/spatial-frequency representations", Proc. SPIE 1666, Human Vision, Visual Processing, and Digital Display III, (27 August 1992); https://doi.org/10.1117/12.135969

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KEYWORDS

Image compression

Image quality

Visualization

Fourier transforms

Image processing

Transform theory

Human vision and color perception

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