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The explosive growth of multimedia data, both on the Internet and in domainspecific applications, has produced a pressing need for techniques that support efficient storage, transmission, and retrieval of such information. Indexing (Chapters 7, 14, and 15) and compression (Chapter 8) are complementary methods that facilitate storage, search, retrieval and transmission of imagery, and other multimedia data types. | Image Databases Search and Retrieval of Digital Imagery Edited by Vittorio Castelli Lawrence D. Bergman Copyright 2002 John Wiley Sons Inc. ISBNs 0-471-32116-8 Hardback 0-471-22463-4 Electronic 16 Compressed or Progressive Image Search SETHURAMAN PANCHANATHAN Arizona State University Tempe Arizona INTRODUCTION The explosive growth of multimedia data both on the Internet and in domainspecific applications has produced a pressing need for techniques that support efficient storage transmission and retrieval of such information. Indexing Chapters 7 14 and 15 and compression Chapter 8 are complementary methods that facilitate storage search retrieval and transmission of imagery and other multimedia data types. The voluminous nature of visual information requires the use of compression techniques in particular of lossy methods. Several compression schemes have been developed to reduce the inherent redundancy present in visual data. Recently the International Standards Organization and CCITT have proposed a variety of standards for still image and video compression. These include JPEG MPEG-1 MPEG-2 and . In addition compression standards for high-resolution images content-based coding and manipulation of visual information such as JPEG 2000 and MPEG-4 have been recently defined. These standardization efforts highlight the growing importance of image compression. Typically indexing and compression are pursued independently Fig. and the features used for indexing are extracted directly from the pixels of the original image. Many of the current techniques for indexing and navigating repositories containing compressed images require the decompression of all the data in order to locate the information of interest. If the features were directly extracted in the compressed domain the need to decompress the visual data and to apply pixel-domain-indexing techniques would be obviated. Compressed-domain indexing CDI techniques are therefore important for .

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