Lossless hyperspectral image compression using intraband and interband predictors

Mamatha, A. S.; Singh, Vaibhav

doi:10.1109/icacci.2014.6968457

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The table above provides a detailed comparison of several compression algorithms, with varying metrics that signify their efficiency and practicality in different scenarios. Among these, the study presented in [35] exhibits the highest compression ratio, which reaches up to 5.5, indicating its effectiveness in reducing data size. Conversely, the method with the lowest power consumption is found in [16], consuming just 0.149 Watts, demonstrating its suitability for energy-critical applications.…”

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

confidence: 98%

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Electronics

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In the field of satellite imaging, effectively managing the enormous volumes of data from remotely sensed hyperspectral images presents significant challenges due to the limited bandwidth and power available in spaceborne systems. In this paper, we describe the hardware acceleration of a highly efficient lossless compression algorithm, specifically designed for real-time hyperspectral image processing on FPGA platforms. The algorithm utilizes an innovative seed generation method for square root calculations to significantly boost data throughput and reduce energy consumption, both of which represent key factors in satellite operations. When implemented on the Cyclone V FPGA, our method achieves a notable operational throughput of 1598.67 Mega Samples per second (MSps) and maintains a power requirement of under 1 Watt, leading to an efficiency rate of 1829.1 MSps/Watt. A comparative analysis with existing and related state-of-the-art implementations confirms that our system surpasses conventional performance standards, thus facilitating the efficient processing of large-scale hyperspectral datasets, especially in environments where throughput and low energy consumption are prioritized.

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

confidence: 98%

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Electronics

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“…Edge detection-based algorithms are covered in references [ 93 , 95 , 112 , 113 , 114 , 115 ]. Edge detectors are image processing methods used to identify discontinuities in the image in terms of changes in brightness.…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

“…A compression ratio of 3.74 is achieved at a throughput of 16.5 MSps utilizing Xilinx Spartan3 FPGA with an embedded ARM processor. Similarly, the median prediction is employed for intra-band prediction in [ 115 ]. However, the inter-band prediction is carried out using a hybrid predictor that combines linear prediction and context prediction.…”

Section: Hardware-accelerated Compression Algorithms Of Hsimentioning

confidence: 99%

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A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

Altamimi

Youssef

2021

Sensors

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Hyperspectral imaging is an indispensable technology for many remote sensing applications, yet expensive in terms of computing resources. It requires significant processing power and large storage due to the immense size of hyperspectral data, especially in the aftermath of the recent advancements in sensor technology. Issues pertaining to bandwidth limitation also arise when seeking to transfer such data from airborne satellites to ground stations for postprocessing. This is particularly crucial for small satellite applications where the platform is confined to limited power, weight, and storage capacity. The availability of onboard data compression would help alleviate the impact of these issues while preserving the information contained in the hyperspectral image. We present herein a systematic review of hardware-accelerated compression of hyperspectral images targeting remote sensing applications. We reviewed a total of 101 papers published from 2000 to 2021. We present a comparative performance analysis of the synthesized results with an emphasis on metrics like power requirement, throughput, and compression ratio. Furthermore, we rank the best algorithms based on efficiency and elaborate on the major factors impacting the performance of hardware-accelerated compression. We conclude by highlighting some of the research gaps in the literature and recommend potential areas of future research.

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An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

Thiyagarajan¹,

Gnanadurai²

2017

PFG

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Lossless hyperspectral image compression using intraband and interband predictors

Cited by 6 publications

References 12 publications

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

Leveraging Seed Generation for Efficient Hardware Acceleration of Lossless Compression of Remotely Sensed Hyperspectral Images

A Systematic Review of Hardware-Accelerated Compression of Remotely Sensed Hyperspectral Images

An Improved Compression Algorithm for Hyperspectral Images based on DVAT-SVD

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