A New Automatic On-Board Multispectral Image Compression System for Leo Earth Observation Satellites

Yu, Guoxia; Vladimirova, Tanya; Sweeting, Martin

doi:10.1109/icdsp.2007.4288602

Cited by 9 publications

(5 citation statements)

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“…Thus the lesser correlation between odd and even column pixels will suppress the compression performance. In [6], a method called Brightness Difference Compensation (BDC) is reported, which is able to bring 5.5% further data reduction on JPEG-LS. BDC is applied to images on a tileby-tile basis with a tile size of 512 by 512 pixels.…”

Section: -D Prediction and 3-d Extensionmentioning

confidence: 98%

An Efficient On-Board Lossless Compression Design for Remote Sensing Image Data

Vladimirova

Sweeting

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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In the Consultative Committee for Space Data SystemsLossless Data Compression (CCSDS-LDC) algorithm a small region of image data is compressed independently to achieve error-resilience functionality, which is one of the most distinct requirements for space applications. 2-D or 3-D predictions are believed to bring superior compression performance, however they interfere with the error-resilience property. This paper proposes a new scan scheme to address the problem. By combination of multidimensional predication and embedded radiometric calibration technique, the proposed system performs very effectively on remote sensing panchromatic and multispectral images. Finally a high-level reconfigurable model for the hardware implementation of the proposed system is developed targeted at FPGA devices.

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Section: -D Prediction and 3-d Extensionmentioning

confidence: 98%

An Efficient On-Board Lossless Compression Design for Remote Sensing Image Data

Vladimirova

Sweeting

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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“…GPUs over the years have evolved into massively-parallel multicore systems that are used to solve compute-intensive real-life problems such as bio-engineering, real-time rendering to exploring new life forms on far away galaxies [2,8]. GPU in combination with the CPU also termed as GPU+CPU help in executing compute-intensive codes in parallel whereas the sequential part of the code continues it execution in the CPU.…”

Section: Gpu Computing Architecturementioning

confidence: 99%

“…Martin Sweeting in his work [8] states about having uncompressed reference samples between sets of data. Thus the error is limited to a certain region called ICR (Independent Compression Region) [9].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

On-board Implementation of Fractal Compression of Satellite Images using Distributed Networked GPUs

Chauhan¹,

Sharmi²,

Al-sideiri³

2013

IJCA

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On-board image compression has been a growing trend in most recent satellite missions. Since majority of satellite applications deal with imagery; compression of images due to limited on-board data storage mediums has become a necessity. The idea of treating satellite imageries as fractals and then encoding them provides an efficient way of conserving bandwidth and per-bit storage costs. Fractal encoding is characterized by slow encoding times which somehow had hindered its popularity in spite of its impressive compression ratio scaling many orders as compared to JPEG. In order to circumvent this handicap, Fractal compression is implemented using powerful GPUs (Graphical Processor Units) that are capable of reaching astronomical computing speeds of around 900 GFLOPS (Quadro Graphic Cards from Nvidia TM ) with internal memory bandwidth ranging to 100 GB/s. This astounding parallel capability is probed to be used on board systems, providing much needed boost for image compression. As the decoding part of compressed fractal images is almost instantaneous, this part can be handled without any specific hardware at the ground station level. Further, the issue of on-board data storage mechanisms is discussed with emphasis on use of HDD instead of SSD and flash memories. In sum, the prime aim is to provide a seamless image compression mechanism coupled with decompression at ground station level thus providing real-time streaming of satellite images from satellite to the ground. General TermsFractal Image Compression, Graphical Processing Unit (GPU), Satellite images, On-board systems, et. al.

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“…Thus the lesser correlation between odd and even column pixels will suppress the compression performance. In [6], a method called Brightness Difference Compensation (BDC) is reported, which is able to bring 5.5% further data reduction on JPEG-LS. BDC is applied to images on a tile-by-tile basis with a tile size of 512 by 512 pixels.…”

Section: Proposed Scanning Scheme and 2-d Predictionmentioning

confidence: 99%