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10 November 2004 Review of CCSDS-ILDC and JPEG2000 coding techniques for remote sensing
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High resolution images are becoming a natural source of data for many different applications, for instance, remote sensing (RS) and geographic information systems (GIS). High resolution is to be understood as a combination of increasing spectral size, increasing spatial resolution per pixel, increasing bit depth resolution per pixel, and larger areas captured at once by the sensors. These images have, therefore, an increasing demand for both storage and transmission scenarios, so that there is a need for compression. Lossless coding, achieving at most 4:1 compression ratios, is seldom enough for applications without a great demand for visual detail. Lossy coding, that may well achieve over 200:1 compression ratios, may still be useful for some final user applications. We are interested in those lossy coding techniques that may fulfill the particular requirements of RS and GIS applications, i.e.: 1) availability of compression of both mono-band and multi-band (either multi or hyperspectral images); 2) high speed of data recovering (from the encoded bit stream) in all image regions, considering also embedded transmission; 3) zoom and lateral shift capability; 4) respect of no-data or meta-data regions, which should be maintained at any compression ratio; 5) in the case of lossy compression, lossless encoding of some physical parameters such as temperature, radiance, elevation, etc.; 6) to reach high compression ratios while maintaining the image quality. In this paper we review two such lossy coding techniques, namely the CCSDS-ILDC Recommendation and the recent JPEG2000 Standard.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joan Serra-Sagrista, Francesc Auli-Llinas, Fernando Garcia-Vilchez, and Cristina Fernandez-Cordoba "Review of CCSDS-ILDC and JPEG2000 coding techniques for remote sensing", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004);

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