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

Yu, Guoxia; Vladimirova, Tanya; Sweeting, Martin

doi:10.1109/igarss.2008.4779158

Cited by 10 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…without radiometric calibration [6]) was obtained from ALOS satellite having resolution of 2.5 meter whereas MS image data-set (with and without radiometric calibration) came from SPOT6 satellite with a resolution of 6 meters. The results are outlined in Table 1 and Table 2 Tables 1 and 2 it is observed that fractal compression rates using GPUs is at par with JPEG compression rates if not better.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Even though Yu et al [10], [11] have proposed a method of destriping for lossless compression, CCD noise effect on lossless compression is still remaining to be adequately considered.…”

Section: Introductionmentioning

confidence: 99%

CCD Noise Effect on Data Transmission Efficiency of Onboard Lossless-Compressed Remote Sensing Images

Chi

Wang

2009

2009 International Conference on Information Engineering and Computer Science

View full text Add to dashboard Cite

It has to be aware that remote sensing images need to be encoded onboard using lossless compression method if all information of the image is required to be fully conserved for ground use. Also, to acquire as much onboard data as possible during one available pass of the satellite, it is necessary to consider efficiency of onboard data compression and transmission between satellite and ground segment. However, noise inevitably induced from CCD when the image is captured will affect efficiency of data compression and transmission. Therefore, main CCD noises and their effect on transmission of lossless-compressed Beijing-1 panchromatic images were investigated in this paper. It is proved that CCD noise should be considered adequately if requiring increasing lossless compression ratio and data transmission efficiency. It is further indicated that shot and readout noise modeled as Poisson noise and, thermal and dark-current noise modeled as Gaussian noise are principle factors for data transmission efficiency; impulse noise induced by CCD detector malfunctions has the least effect on data transmission efficiency. Finally, a FPGA-based model of noise suppression is proposed for future development of onboard data processing system.

show abstract

A high‐throughput flexible lossless compression and decompression architecture for color images

Xu,

Yao,

et al. 2024

Circuit Theory & Apps

View full text Add to dashboard Cite

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution.

show abstract

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

Cited by 10 publications

References 8 publications

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

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

CCD Noise Effect on Data Transmission Efficiency of Onboard Lossless-Compressed Remote Sensing Images

A high‐throughput flexible lossless compression and decompression architecture for color images

Contact Info

Product

Resources

About