A generic, cluster-centred lossless compression framework for joint auroral data
| Autor: | Tan Qu, Jiaji Wu, Kun Shang, Wanqiu Kong, Witold Pedrycz, Zejun Hu |
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| Rok vydání: | 2021 |
| Předmět: |
Lossless compression
Computer science Real-time computing Defense Meteorological Satellite Program 020207 software engineering 02 engineering and technology Hierarchical clustering Coupling (computer programming) Signal Processing 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Spectrograph Data reduction Volume (compression) Data transmission |
| Zdroj: | Journal of Visual Communication and Image Representation. 78:103185 |
| ISSN: | 1047-3203 |
| Popis: | Studying the well-known phenomenon “aurora” plays a pivotal role in investigating the solar–terrestrial coupling mechanism. A special auroral spectrograph in Antarctic Zhongshan Station constitutes a auroral observation joint system with satellite-borne sensors of the Defense Meteorological Satellite Program. Multipoint observation by this system provides more essential information for relevant studies than single observation by each instrument, but also results in a multifold increased volume of data that are difficult to be either stored or transmitted. To address this difficulty, we develop a clustering-based, generic lossless data compression framework that combines the usage of various ultimate compressors with a hierarchical clustering algorithm to exert the strength of all the compressors in data reduction. This framework achieves an always-best compression performance for different-sized datasets with a reasonable time consumption, which promises the design of pipelines using it for real-time data transmission. |
| Databáze: | OpenAIRE |
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