Image sequence coding using concepts in visual perception

The utilization of digital image sequences is becoming increasingly important in modern imaging applications, includingHDTV, interactive video, teleconferencing, telerobotics, and medical imaging. Due to the immense amountof data in image sequences, high compression coding methods are critical for efficient transmission and storage.Often, compression ratios exceeding 200:1 are required. Because these ratios are near the limits of conventionalcoding methods, we are investigating alternative methods which take human visual perception into account. In thisway, sequences can be coded such that only the most perceptually important information is retained. Techniques ofthis type, known as "second generation" coding methods, have proven very successful for the compression of singleimages. In this paper, we show that these methods are also effective for image sequence coding, and that they arecapable of delivering the high compression ratios required for present and future applications. Five different sequencecoding methods, following this basic philosophy, are discussed: coding via 3-D split-and-merge, edge-based coding,segmentation-based coding using Gibbs-Markov random fields, the application of the Gabor decomposition to coding,and the use of polar separable quadrature mirror filters.

Published in:
Proceedings of SPIE, 1249, 272-283
Presented at:
Human Vision and Electronic Imaging: Models, Methods, and Applications

 Record created 2007-11-08, last modified 2018-01-28

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