Abstract
This article studies the performance impact related to different parameter choices for the new CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression standard. This standard supersedes CCSDS-123.0-B-1 and extends it by incorporating a new near-lossless compression capability, as well as other new features. This article studies the coding performance impact of different choices for the principal parameters of the new extensions, in addition to reviewing related parameter choices for existing features. Experimental results include data from 16 different instruments with varying detector types, image dimensions, number of spectral bands, bit depth, level of noise, level of calibration, and other image characteristics. Guidelines are provided on how to adjust the parameters in relation to their coding performance impact.
Highlights
It is well known that space-borne remote-sensing instruments are often the source of large volumes of data and that, due to constraints on the down-link channel, these data need to be compressed [1,2,3]
The new Consultative Committee for Space Data Systems (CCSDS)-123.0-B-2 provides more than twenty configuration parameters, with most of these parameters having a direct impact on the compression performance of an implementation
The choices of local sum type, prediction mode, and number of prediction bands (P) determine the prediction neighborhood, i.e., the neighboring samples that directly influence the prediction of a given image sample
Summary
It is well known that space-borne remote-sensing instruments are often the source of large volumes of data and that, due to constraints on the down-link channel, these data need to be compressed [1,2,3]. Employing different settings for these parameters may allow an implementer to achieve different trade-offs between implementation complexity and compression efficiency or may allow an end-user to fine-tune compression performance for particular datasets. This paper studies these parameters and provides some guidelines on how to adjust them to achieve high coding efficiency based on a representative corpus of multi- and hyper-spectral images. The current study includes revisiting our previous assessment of parameter settings under lossless compression [21,22] while considering the newly-available coding options, in addition to providing new guidelines for both old and new coding options for near-lossless compression.
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