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Proceedings Paper

Content-based image and video compression
Author(s): Xun Du; Honglin Li; Stanley C. Ahalt
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Paper Abstract

The term Content-Based appears often in applications for which MPEG-7 is expected to play a significant role. MPEG-7 standardizes descriptors of multimedia content, and while compression is not the primary focus of MPEG-7, the descriptors defined by MPEG-7 can be used to reconstruct a rough representation of an original multimedia source. In contrast, current image and video compression standards such as JPEG and MPEG are not designed to encode at the very low bit-rates that could be accomplished with MPEG-7 using descriptors. In this paper we show that content-based mechanisms can be introduced into compression algorithms to improve the scalability and functionality of current compression methods such as JPEG and MPEG. This is the fundamental idea behind Content-Based Compression (CBC). Our definition of CBC is a compression method that effectively encodes a sufficient description of the content of an image or a video in order to ensure that the recipient is able to reconstruct the image or video to some degree of accuracy. The degree of accuracy can be, for example, the classification error rate of the encoded objects, since in MPEG-7 the classification error rate measures the performance of the content descriptors. We argue that the major difference between a content-based compression algorithm and conventional block-based or object-based compression algorithms is that content-based compression replaces the quantizer with a more sophisticated classifier, or with a quantizer which minimizes classification error. Compared to conventional image and video compression methods such as JPEG and MPEG, our results show that content-based compression is able to achieve more efficient image and video coding by suppressing the background while leaving the objects of interest nearly intact.

Paper Details

Date Published: 1 August 2002
PDF: 11 pages
Proc. SPIE 4727, Algorithms for Synthetic Aperture Radar Imagery IX, (1 August 2002); doi: 10.1117/12.478687
Show Author Affiliations
Xun Du, The Ohio State Univ. (United States)
Honglin Li, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 4727:
Algorithms for Synthetic Aperture Radar Imagery IX
Edmund G. Zelnio, Editor(s)

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