FPGA Implementation of the CCSDS 1.2.3 Standard for Real-Time Hyperspectral Lossless Compression

Báscones, Daniel; González, Carlos Villaseca; Mozos, Daniel

doi:10.1109/jstars.2017.2767680

Cited by 26 publications

(26 citation statements)

References 18 publications

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“…The core supports the majority of user-defined compression parameters proposed by the standard. The achieved throughput is higher in comparison with the state of the art implementations [14]- [18].…”

Section: Introductionmentioning

confidence: 90%

“…In BSQ ordering, the frames are processed sequentially, implying that the largest sampling distance is P × N x × N y cycles. TABLE II: Sample distance between the current sample and the sample in the same position in the previous bands [18] Order…”

Section: Proposed Ccsds-123 Encoder Implementationmentioning

confidence: 99%

“…The resulting bitstream was successfully compared to the expected compressed image bitstream. The proposed CCSDS-123 implementation is compared with recent state of the art FGPA implementations [14]- [18] as presented in Table XI. The sensor maximum data rates for AVIRIS, AVIRIS NG and HICO imagers are listed representing real-time constraint for data processing.…”

Section: B Verificationmentioning

confidence: 99%

“…The latest CCSDS-123 standard [12] is characterized by low complexity suitable for fast real-time hardware implementation. Recent FPGA implementations of the CCSDS-123 standard [13] presented in [14]- [18] target high throughput and take into account limitations in terms of storage utilization.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression Standard for Hyperspectral Images

Fjeldtvedt

Orlandić

Johansen

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral imaging (HSI) can extract information from scenes on the Earth surface acquired by airborne or spaceborne sensors. On-board processing of hyperspectral imaging is characterized by large datasets on one side and limited processing time and communication links on the other. The CCSDS-123 algorithm is a compression standard assembled for space-related application which allows compacted data transmission via a transmission link. In this paper, a low-complexity highthroughput FPGA implementation of CCSDS-123 compression algorithm with BIP ordering is presented. Hardware accelerators implemented in Field-Programmable Gate Arrays (FPGAs) are increasingly used for custom tasks due to their efficiency, low power and reconfigurability. The proposed implementation of CCSDS-123 compression standard has been tested on Zedboard development board containing a Zynq-7020 FPGA. The results are verified against an existing software implementation. The synthesized design can perform on-the-fly processing of hyperspectral images with maximum operating frequency of 147 MHz. The achieved throughput of 147 Msamples/s (2.35 Gb/s) is higher when compared with the throughput reported in recent state of the art FPGA implementations.Index Terms-CCSDS-123 compression, Field programmable gate arrays (FPGA), HSI, lossless compression, space-related applications J.Fjeldtvedt and M. Orlandić is with the

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Section: Introductionmentioning

confidence: 90%

Section: Proposed Ccsds-123 Encoder Implementationmentioning

confidence: 99%

Section: B Verificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression Standard for Hyperspectral Images

Fjeldtvedt

Orlandić

Johansen

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Very recently, the CCSDS has superseded Issue 1 of the Lossless Multispectral & Hyperspectral Image Compression standard [7] with Issue 2 titled Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image Compression (CCSDS-123.0-B-2) [8]. The original issue of the standard employed the fast lossless compression algorithm [9,10] to achieve state-of-the-art compression performance, whilst being implemented in resource-constrained hardware available for space operation [11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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A residual network framework based on weighted feature channels for multispectral image compression

Kong¹,

Zhao²,

Li³

et al. 2020

Ad Hoc Networks

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FPGA Implementation of the CCSDS 1.2.3 Standard for Real-Time Hyperspectral Lossless Compression

Cited by 26 publications

References 18 publications

An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression Standard for Hyperspectral Images

An Efficient Real-Time FPGA Implementation of the CCSDS-123 Compression Standard for Hyperspectral Images

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

A residual network framework based on weighted feature channels for multispectral image compression

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