Image sequence coding using concepts in visual perception

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Abstract
The utilization of digital image sequences is becoming increasingly important in modern imaging applications, including HDTV, interactive video, teleconferencing, telerobotics, and medical imaging. Due to the immense amount of 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 conventional coding methods, we are investigating alternative methods which take human visual perception into account. In this way, sequences can be coded such that only the most perceptually important information is retained. Techniques of this type, known as "second generation" coding methods, have proven very successful for the compression of single images. In this paper, we show that these methods are also effective for image sequence coding, and that they are capable of delivering the high compression ratios required for present and future applications. Five different sequence coding 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.

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