Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

Chow, Kevin; Tzamarias, Dion Eustathios Olivier; Hernández-Cabronero, Miguel; Blanes, Ian; Serra-Sagristà, Joan

doi:10.3390/rs12121983

Cited by 6 publications

(11 citation statements)

References 32 publications

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“…Four variations are employed in our experiments using the Corpus dataset of HSIs. Our performance results yield an enhancement in data reduction that reaches 29.89% when comparing the corresponding geometric mean value with that obtained by the state-of-the-art k 2 -raster method [40]. • A novel near-lossless compression of HSIs is also proposed by incorporating our published quadrature-based square rooting method [39].…”

Section: Introductionmentioning

confidence: 78%

“…The k 2 -tree structure serves as a compact representation of the adjacency matrix for a directed graph [63]. Chow et al also suggest that the k 2 -raster structure works best when combined with directly addressable codes (DACs) [40,62]. We conclude this review of recent research works on HSI compression by summarizing the key aspects of the above-mentioned studies in Table 1.…”

Section: Related Workmentioning

confidence: 95%

“…Finally, the works by Chow et al describe the compression of hyperspectral images by utilizing a k 2 -raster compact data structure that achieves a compression ratio similar to classical techniques, enabling direct access without decompression [40,62]. The k 2 -tree structure serves as a compact representation of the adjacency matrix for a directed graph [63].…”

Section: Related Workmentioning

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: Introductionmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

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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“…We used integer encoding to convert each word to numerical data. We chose this method since it is very appropriate for a dataset with a larger size, straightforward to implement, and does not expand the feature space [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

2022

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The next word prediction is useful for the users and helps them to write more accurately and quickly. Next word prediction is vital for the Amharic Language since different characters can be written by pressing the same consonants along with different vowels, combinations of vowels, and special keys. As a result, we present a Bi-directional Long Short Term-Gated Recurrent Unit (BLST-GRU) network model for the prediction of the next word for the Amharic Language. We evaluate the proposed network model with 63,300 Amharic sentence and produces 78.6% accuracy. In addition, we have compared the proposed model with state-of-the-art models such as LSTM, GRU, and BLSTM. The experimental result shows, that the proposed network model produces a promising result.

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“…The paper "Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k 2 -Raster Compact Data Structure" by Chow, K., Tzamarias, D.E.O., Hernández-Cabronero, M., Blanes, I., Serra-Sagristà, J. [1], examines various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This structure leads to a compression ratio similar to that produced by some classical compression techniques while also providing direct access for query to its data elements without requiring any decompression.…”

Section: Overview Of the Issue: Remote Sensing Data Compressionmentioning

confidence: 99%

Editorial to Special Issue “Remote Sensing Data Compression”

Vozel

Serra-Sagristà

2021

Remote Sensing

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A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

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Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

Cited by 6 publications

References 32 publications

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

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

Bi-directional long short term memory-gated recurrent unit model for Amharic next word prediction

Editorial to Special Issue “Remote Sensing Data Compression”

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