Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

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

doi:10.3390/rs9100973

Cited by 24 publications

(27 citation statements)

References 10 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparison of the proposed parallel implementation of CCSDS-123 algorithm with recent sequential [12][13][14][15]17,18] and parallel [16] FPGA implementations with regards to maximum frequency, the throughput performance and power is presented in Table 4. The majority of implementations target Virtex-5 FX130T FPGA which is commercial equivalent of radiation hardened Virtex-5QV.…”

Section: Comparison With State-of-the-art Implementationsmentioning

confidence: 99%

“…In particular, the CCSDS-123 compression standard [10,11] is an efficient prediction-based algorithm characterized by low complexity and, thus, is suitable for real-time hardware implementation. In fact, in the recent years several FPGA implementations of the CCSDS-123 standard are presented in the literature [12][13][14][15][16][17][18][19]. Keymeulen et al [12] propose an on-the-fly implementation in BIP sample ordering.…”

Section: Introductionmentioning

confidence: 99%

“…This is achieved by queuing incoming samples in internal FIFOs, resulting in linear dependence of memory usage with respect to the product of width and depth of a HSI cube. A parallel CCSDS-123 implementation proposed by Báscones et al [16] consists of several instances of the CCSDS-123 core that share local differences. Other than sharing local differences, the cores operate independently by processing samples from a fixed subset of bands.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

2019

View full text Add to dashboard Cite

Satellite onboard processing for hyperspectral imaging applications is characterized by large data sets, limited processing resources and limited bandwidth of communication links. The CCSDS-123 algorithm is a specialized compression standard assembled for space-related applications. In this paper, a parallel FPGA implementation of CCSDS-123 compression algorithm is presented. The proposed design can compress any number of samples in parallel allowed by resource and I/O bandwidth constraints. The CCSDS-123 processing core has been placed on Zynq-7035 SoC and verified against the existing reference software. The estimated power use scales approximately linearly with the number of samples processed in parallel. Finally, the proposed implementation outperforms the state-of-the-art implementations in terms of both throughput and power.

show abstract

Section: Comparison With State-of-the-art Implementationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

2019

View full text Add to dashboard Cite

show abstract

“…Consequently, an investigation on the strategies for optimizing the computational performance for an on-board implementation, especially by means on a FPGA, is outside the scope of this paper. In any case, a detailed discussion on such a topic can be found in [39]. In this work, the compression and decompression stages have used a JAVA version of the EMPORDA software featuring the standard CCSDS 1.2.3 algorithm.…”

Section: Computational Performancementioning

confidence: 99%

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

et al. 2019

View full text Add to dashboard Cite

A noticeable topic to be pursued in the field of on-board real-time data processing is the influence of the modulation transfer function (MTF) of the image acquisition system on the lossless compressibility of raw (that is, uncalibrated) hyperspectral data. Actually, notwithstanding the system device is constrained by several design and manufacturing requirements, the impact of the on-board MTF on the performance of data compressors is becoming remarkable. In particular, the aim of reducing both transmission bandwidth/power and mass storage can be efficiently pursued. Such an analysis is expected to be useful especially for systems employed in mini-satellites, whose payload must be compact and light. From this perspective, this paper investigates the performance of a typical imaging system that acquires low/medium-spatial-resolution images, by considering high-resolution reference data, which simulate the real scene to be imaged. To this end, standard Consultative Committee for Space Data Systems (CCSDS) Aviris 2006 data have been chosen, due to their spatial resolution of 17 m, which is adequate to be a reference for simulated data whose spatial resolution is foreseen between 50 and 150 m. MTF requirements are usually provided based on the cut-off value of the amplitude at the Nyquist frequency, which is defined as a half of the sampling frequency. Typically, a cut-off value between 0 . 2 and 0 . 3 ensures that a sufficient amount of information is delivered from the scene to the acquired image, by avoiding at the same time the degradation due to an excessive aliasing distortion. All the scores are achieved by running the standard lossless compression scheme CCSDS 1.2.3.0-B-1 for multispectral/hyperspectral data, as a function of the cut-off value and different noise acquisition levels. The final results, and related plots, show that this analysis can suggest a suitable choice for the cut-off value, to ensure both a sufficient quality and low bit rates for the transmitted data to the ground station.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

Cited by 24 publications

References 10 publications

A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

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

Contact Info

Product

Resources

About