The CCSDS 123.0-B-2 "Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression" Standard: A comprehensive review

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, "Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression" standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compress...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing magazine Vol. 9; no. 4; pp. 102 - 119
Main Authors: Hernandez-Cabronero, Miguel, Kiely, Aaron B., Klimesh, Matthew, Blanes, Ian, Ligo, Jonathan, Magli, Enrico, Serra-Sagrista, Joan
Format: Journal Article
Language:English
Published: IEEE 01.12.2021
Subjects:
Compressors
Field programmable gate arrays
Hardware
Image coding
Quantization (signal)
Throughput
Tutorials
ISSN:2473-2397, 2168-6831
Online Access:Get full text
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Summary:The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-B-2, "Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression" standard. This standard extends the previous issue, CCSDS 123.0-B-1, which supported only lossless compression, while maintaining backward compatibility. The main novelty of the new issue is support for near-lossless compression, i.e., lossy compression with user-defined absolute and/or relative error limits in the reconstructed images. This new feature is achieved via closed-loop quantization of prediction errors. Two further additions arise from the new near-lossless support: first, the calculation of predicted sample values using sample representatives that may not be equal to the reconstructed sample values, and, second, a new hybrid entropy coder designed to provide enhanced compression performance for low-entropy data, prevalent when nonlossless compression is used. These new features enable significantly smaller compressed data volumes than those achievable with CCSDS 123.0-B-1 while controlling the quality of the decompressed images. As a result, larger amounts of valuable information can be retrieved given a set of bandwidth and energy consumption constraints.
ISSN:2473-2397
2168-6831
DOI:10.1109/MGRS.2020.3048443