Now it is still a challenge to compress high-throughput hyperspectral tensor image data on lightweight air-carried/spaceborne remote sensing systems, primarily due to insufficient computational resources and limited t...
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Now it is still a challenge to compress high-throughput hyperspectral tensor image data on lightweight air-carried/spaceborne remote sensing systems, primarily due to insufficient computational resources and limited transmission bandwidth. To address this challenge, we propose a bit-level tensor data compression network (BTC-Net) that provides higher compression performance by leveraging a data-driven lightweight quantized neural encoder with two-stage bitcompression. The BTC-Net achieves semantic near-lossless high reconstruction quality at low compressionbit rates thanks to its optimized decoder, which uses a channelwise attention-based enhancement module to recover hyperspectral tensor data. Experimental results on different hyperspectral datasets show that the BTC-Net could achieve an extremely low compressionbit rate of fewer than 0.04 bits per pixel per band (bpppb) with the state-of-the-art (SOTA) reconstruction performances.
Algorithmic data compression is a crucial concept in computer science aimed at reducing the size of stored data to minimize storage space. Particularly significant is the ability of an algorithm to compress data to th...
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ISBN:
(纸本)9798350381771;9798350381764
Algorithmic data compression is a crucial concept in computer science aimed at reducing the size of stored data to minimize storage space. Particularly significant is the ability of an algorithm to compress data to the bitlevel, which greatly enhances the efficiency of storing vast amounts of data today. This paper introduces a novel approach to bit-level text compression algorithms, called BATCA. It features a distinctive dictionary design based on patterns of word construction, including vowel, prefix, suffix, and article formats, facilitating word referencing during both compression and decompression phases. Theoretical findings reveal significant compression achievements: a maximum bitcompression per word of 69.79% with ASCII encoding, 84.89% with Unicode, and 92.45% with UTF-8, resulting in a maximum compression ratio of 16.55 times. Additionally, empirical evaluations on real data from the Calgary and ArTechnica corpora, compared against common applications, demonstrate the algorithm's ability to significantly outperform existing alternatives in terms of saved space percentage and compression ratios.
In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the ...
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In this paper, the authors present a description of a new Web search engine model, the compressed index-query (CIQ) Web search engine model. This model incorporates two bit-level compression layers implemented at the back-end processor (server) side, one layer resides after the indexer acting as a second compression layer to generate a double compressed index (index compressor), and the second layer resides after the query parser for query compression (query compressor) to enable bit-level compressed index-query search. The data compression algorithm used in this model is the Hamming codes-based data compression (HCDC) algorithm, which is an asymmetric, lossless, bit-level algorithm permits CIQ search. The different components of the new Web model are implemented in a prototype CIQ test tool (CIQTT), which is used as a test bench to validate the accuracy and integrity of the retrieved data and evaluate the performance of the proposed model. The test results demonstrate that the proposed CIQ model reduces disk space requirements and searching time by more than 24%, and attains a 100% agreement when compared with an uncompressed model.
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