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Edge and Mean Based Image Compression

Author(s)
Desai, Ujjaval Y.; Mizuki, Marcelo M.; Masaki, Ichiro; Horn, Berthold K.P.
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Abstract
In this paper, we present a static image compression algorithm for very low bit rate applications. The algorithm reduces spatial redundancy present in images by extracting and encoding edge and mean information. Since the human visual system is highly sensitive to edges, an edge-based compression scheme can produce intelligible images at high compression ratios. We present good quality results for facial as well as textured, 256~x~256 color images at 0.1 to 0.3 bpp. The algorithm described in this paper was designed for high performance, keeping hardware implementation issues in mind. In the next phase of the project, which is currently underway, this algorithm will be implemented in hardware, and new edge-based color image sequence compression algorithms will be developed to achieve compression ratios of over 100, i.e., less than 0.12 bpp from 12 bpp. Potential applications include low power, portable video telephones.
Date issued
1996-11-01
URI
http://hdl.handle.net/1721.1/5943
Other identifiers
AIM-1584
Series/Report no.
AIM-1584
Keywords
AI, MIT, Artificial Intelligence

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