Onboard Near-Lossless Data Compression Techniques
DOI: 10.1117/3.1002297.ch5
text A A A


5.1 Near-Lossless Satellite Data Compression5.2 Cluster SAMVQ5.2.1 Organizing continuous data flow into regional datacubes5.2.2 Solution for overcoming the blocking effect5.2.3 Removing the boundary between adjacent regions5.2.4 Attaining a fully redundant regional datacube for preventing data loss in the downlink channel5.2.5 Compression performance comparison between SAMVQ and cluster SAMVQ5.3 Recursive HSOCVQ5.3.1 Reuse of codevectors of the previous region to attain a seamless conjunction between regions5.3.2 Training codevectors for a current frame and applying them to subsequent frames5.3.3 Two schemes of carrying forward reused codevectors trained in the previous region5.3.4 Compression performance comparison between baseline and recursive HSOCVQ5.4 Evaluation of Near-Lossless Performance of SAMVQ and HSOCVQ5.4.1 Evaluation method and test dataset5.4.2 Evaluation of a single spectrum5.4.3 Evaluation of an entire datacube5.5 Evaluation of SAMVQ with Regard to the Development of International Standards of Spacecraft Data Compression5.5.1 CCSDS test datasets5.5.2 Test results of hyperspectral datasets5.5.3 Compression of multispectral datasets using SAMVQReferences

© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

Access This Chapter
Please Wait... Processing your request... Please Wait.
Sign In

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections


Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.

Your library does not currently subscribe to eBooks on the SPIE Digital Library. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.

Sign In