Results 231 to 240 of about 1,973 (264)

Efficient Algorithms for On-Line Symbol Ranking Compression

1999
Symbol ranking compression algorithms are known to achieve a very good compression ratio. Off-line symbol ranking algorithms (e.g., bzip, szip) are currently the state of the art for lossless data compression because of their excellent compression/time trade-off. Some on-line symbol ranking algorithms have been proposed in the past.
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Symbol mapping and context filtering for lossless image compression

Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), 2002
Key building blocks of lossless image compression algorithms include adaptive prediction, context-based error feedback and adaptive entropy coding. This paper presents a new algorithm which includes two other building blocks-symbol mapping and context filtering.
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EEDC Image Compression Enhancement by Symbol Prediction

2013
This article investigates the possibility of analyzing the input image data in order to maximize image compression. Edge enhancing diffusion image compression (EEDC) is the basis of this research. Entropy coders by themselves are restricted and can only achieve limited compression. Input data analysis and preparation for entropy coding is used in order
Livada, Časlav, Galić, Irena, Zovko-Cihlar, Branka   +2 more
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Symbolic Compression Algorithms based on Prime Entropy Windows

This paper introduces a novel symbolic compression algorithm that leverages prime entropy windows to achieve efficient, context-aware encoding of structured data. The method utilizes dynamically shifting entropy-guided compression layers based on prime number patterns to compress textual, biological, and log-based datasets.
Rezapour, Majid, Rezapour, Ramin
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Symbolic Compression in Human Cognition: Metaphor as a Compression Layer

This paper proposes a new theoretical framework in which metaphor is recast as a form of symbolic compression—a mechanism by which human cognition transforms high-dimensional experiences into low-dimensional symbolic forms. By examining metaphor not as linguistic flair but as core cognitive infrastructure, the paper argues that metaphor operates like a
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Reducing Symbol Search Overhead on Stream-Based Lossless Data Compression

2019
Lossless data compression is emerged to utilize in the BigData applications in the recent days. The conventional algorithms mainly generate a symbol lookup table to replace a frequent data pattern in the inputted data to a symbol, and then compresses the information.
Shinichi Yamagiwa, Ryuta Morita, Koichi Marumo   +2 more
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A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

ACM Computing Surveys, 2023
Denis Kleyko, Dmitri A Rachkovskij, Evgeny Osipov   +2 more
exaly  

Exploring sustainable development goals reporting practices: From symbolic to substantive approaches—Evidence from the energy sector

Corporate Social Responsibility and Environmental Management, 2022
Francesca Manes-Rossi, Giuseppe Nicolo'
exaly  

AI Feynman: A physics-inspired method for symbolic regression

Science Advances, 2020
Silviu-Marian Udrescu, Max Erik Tegmark
exaly  

Quantifying Repetition in Symbolic Music using Lempel-Ziv Compression

2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 2023
Anton Chen, Ross Greer
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