Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard

Blanes, Ian; Kiely, Aaron; Hernández-Cabronero, Miguel; Serra-Sagristà, Joan

doi:10.3390/rs11111390

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…The spatial transforms compared in this experiment are the proposed IGB, the tiled GraphBior, the DWT, the ISGL Q and ISGL D . In this final experiment, we also include a comparison of our overall compression scheme with the CCSDS-123.0-B-2 standard, tuned according to Reference [19].…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile the techniques used for the lossless compression of hyperspectral images are generally based on a predictive coding model [13,14] though lossy predictive techniques [15] have the benefit of a lower computational complexity. Some lossless predictive techniques [16,17] are implemented in the CCSDS-123.0-B-2 [18] standard that also supports near-lossless compression and whose parameter tuning is explored in Reference [19].…”

Section: Introductionmentioning

confidence: 99%

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Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

et al. 2019

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Hyperspectral images are depictions of scenes represented across many bands of the electromagnetic spectrum. The large size of these images as well as their unique structure requires the need for specialized data compression algorithms. The redundancies found between consecutive spectral components and within components themselves favor algorithms that exploit their particular structure. One novel technique with applications to hyperspectral compression is the use of spectral graph filterbanks such as the GraphBior transform, that leads to competitive results. Such existing graph based filterbank transforms do not yield integer coefficients, making them appropriate only for lossy image compression schemes. We propose here two integer-to-integer transforms that are used in the biorthogonal graph filterbanks for the purpose of the lossless compression of hyperspectral scenes. Firstly, by applying a Triangular Elementary Rectangular Matrix decomposition on GraphBior filters and secondly by adding rounding operations to the spectral graph lifting filters. We examine the merit of our contribution by testing its performance as a spatial transform on a corpus of hyperspectral images; and share our findings through a report and analysis of our results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

et al. 2019

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“…In addition to Golomb power-of-two coding, 16 variable-to-variable codes are defined for low-entropy scenarios. In this work, the default parameters described in [36] are used for CCSDS 123.0-B-2.…”

Section: Low-complexity Compression Methodsmentioning

confidence: 99%

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

et al. 2020

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The capacity of the downlink channel is a major bottleneck for applications based on remote sensing hyperspectral imagery (HSI). Data compression is an essential tool to maximize the amount of HSI scenes that can be retrieved on the ground. At the same time, energy and hardware constraints of spaceborne devices impose limitations on the complexity of practical compression algorithms. To avoid any distortion in the analysis of the HSI data, only lossless compression is considered in this study. This work aims at finding the most advantageous compression–complexity trade-off within the state of the art in HSI compression. To do so, a novel comparison of the most competitive spectral decorrelation approaches combined with the best performing low-complexity compressors of the state is presented. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions. Assuming only a limited amount of energy is available, obtained data suggest that the FAPEC algorithm yields the best trade-off. When compared to the CCSDS 123.0-B-2 standard, FAPEC is 5.0 times faster and its compressed data rates are on average within 16% of the CCSDS standard. In scenarios where energy constraints can be relaxed, CCSDS 123.0-B-2 yields the best average compression results of all evaluated methods.

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“…Among the standards published by the CCSDS, the CCSDS 123.0-B-2 [9][10][11] focuses on the near-lossless compression of multispectral and hyperspectral images, defining an algorithm comprised of two main stages: a highly configurable predictive preprocessor for spectral and spatial decorrelation (see [10] for more details about predictor parameters) and an entropy coder, which represents the output bitstream with the smallest possible number of bits. Concretely, three different entropy coders are proposed, though only one of them is new in Issue 2 of the standard, which is named hybrid encoder.…”

Section: Ccsds 1230-b-2 Algorithmmentioning

confidence: 99%

Hardware Implementation of the CCSDS 123.0-B-2 Near-Lossless Compression Standard Following an HLS Design Methodology

Barrios¹,

Sánchez²,

Guerra³

et al. 2021

Remote Sensing

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The increment in the use of high-resolution imaging sensors on-board satellites motivates the use of on-board image compression, mainly due to restrictions in terms of both hardware (computational and storage resources) and downlink bandwidth with the ground. This work presents a compression solution based on the CCSDS 123.0-B-2 near-lossless compression standard for multi- and hyperspectral images, which deals with the high amount of data acquired by these next-generation sensors. The proposed approach has been developed following an HLS design methodology, accelerating design time and obtaining good system performance. The compressor is comprised by two main stages, a predictor and a hybrid encoder, designed in Band-Interleaved by Line (BIL) order and optimized to achieve a trade-off between throughput and logic resources utilization. This solution has been mapped on a Xilinx Kintex UltraScale XCKU040 FPGA and targeting AVIRIS images, reaching a throughput of 12.5 MSamples/s and consuming only the 7% of LUTs and around the 14% of dedicated memory blocks available in the device. To the best of our knowledge, this is the first fully-compliant hardware implementation of the CCSDS 123.0-B-2 near-lossless compression standard available in the state of the art.

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Performance Impact of Parameter Tuning on the CCSDS-123.0-B-2 Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression Standard

Cited by 18 publications

References 14 publications

Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

Compression of Hyperspectral Scenes through Integer-to-Integer Spectral Graph Transforms

High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

Hardware Implementation of the CCSDS 123.0-B-2 Near-Lossless Compression Standard Following an HLS Design Methodology

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