Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data

Huang, Bormin

doi:10.1007/978-1-4614-1183-3_12

Cited by 2 publications

(3 citation statements)

References 18 publications

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“…A typical lossless compressor consists of a preprocessor followed by an entropy encoder [68]. The preprocessor's main function is to decorrelate the input data, which is then passed to the entropy encoder.…”

Section: Preprocessingmentioning

confidence: 99%

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Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Entropy

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Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic operations, making it suitable for real-time onboard compression. In addition, this near-lossless compressor could be incorporated for hard-to-compress images offering a stabilized reduction at nearly 40% with a maximum relative error of 0.33 and a maximum absolute error of 30. Our results also show that a lossless compression performance, in terms of compression ratio, of up to 2.6 is achieved when testing with hyperspectral images from the Corpus dataset. Further, an improvement in the compression rate over the state-of-the-art k2-raster technique is realized for most of these HSIs by all four variations of our proposed lossless compression method. In particular, a data reduction enhancement of up to 29.89% is realized when comparing their respective geometric mean values.

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Section: Preprocessingmentioning

confidence: 99%

“…We suggest using the well-known Rice coding technique [80] to map the most frequent values into a smaller number of bits. Rice coding is preferable when the data to be compressed follow a geometric statistical distribution [68]. This distribution is usually determined after data have been processed in an earlier stage.…”

Section: Postprocessingmentioning

confidence: 99%

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Entropy

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“…However, the limited resources available on the payload poses challenges on the on-board codecs because of its computational intensive task and high input data rates from the sensor. Basic building blocks of conventional satellite imaging system are imaging sensor, analog to digital converter, radiometric corrector, image compression, entropy coding and RF module for transmission to earth station [1,2]. Remote sensing satellites in low earth orbit (LEO) and geostationary earth orbit (GEO) are placed to provide images for research, agriculture, forestry, environmental mapping, land cover and other atmosphere monitoring applications.…”

Section: Introductionmentioning

confidence: 99%

A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression

Joshi

Rani

2023

IEEE Geosci. Remote Sensing Lett.

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As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression method to meet time, bandwidth and resource requirements with error resilience. This paper proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data that utilizes spectral as well as spatial correlation and at the same time is computationally less complex. Combination of nearest neighbor and non-binary tree traversal method with neighbor driven decision making is used in preprocessing stage. Extended causal neighborhood of current sample is used to minimize the prediction residual, which makes more than 80% calculations causal in nature. The prediction residual is then encoded using sample adaptive Golomb coding. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of algorithm and comparison of data rates with stateof-the-art on-board lossless compression methods shows good results. Further, it shows reduced computational complexity and high error resilience in comparison to other methods.

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Fast Precomputed Vector Quantization with Optimal Bit Allocation for Lossless Compression of Ultraspectral Sounder Data

Cited by 2 publications

References 18 publications

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression

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