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Proceedings Paper

Multiagent system for optimizing filter coefficients in image compression
Author(s): Yi Shang; Longzhuang Li
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Paper Abstract

In this paper, we present a new intelligent agent-based method to design filter banks that maximize compression quality. In this method, a multi-agent system containing cooperating intelligent agents with different roles is developed to search for filter banks that improve image compression quality. The multi-agent system consists of one generalization agent, and several problem formulation, optimization, and compression agents. The generalization agent performs problem decomposition and result collection. It distributes optimization tasks to optimization agents, and later collects results and selects one solution that works well on all training images as the final output. Problem formulation agents build optimization models that are used by the optimization agents. The optimization formulation includes both the overall performance of image compression and metrics of individual filters. The compression performance is provided by the image coding agent. Optimization agents apply various optimization methods to find the best filter bank for individual training images. Our method is modular and flexible, and is suitable for distributed processing. In experiments, we applied the proposed method to a set of benchmark images and designed filter banks that improve the compression performance of existing filter banks.

Paper Details

Date Published: 7 October 1999
PDF: 10 pages
Proc. SPIE 3817, Parallel and Distributed Methods for Image Processing III, (7 October 1999); doi: 10.1117/12.365894
Show Author Affiliations
Yi Shang, Univ. of Missouri/Columbia (United States)
Longzhuang Li, Univ. of Missouri/Columbia (United States)


Published in SPIE Proceedings Vol. 3817:
Parallel and Distributed Methods for Image Processing III
Hongchi Shi; Patrick C. Coffield, Editor(s)

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