Evaluation on high-performance image compaction algorithms in spatio-temporal data processing

被引:0
作者
Li, Guozhang [1 ]
Xing, Kongduo [1 ]
Alfred, Rayner [2 ]
Wang, Yetong [1 ]
机构
[1] Hainan Vocat Univ Sci & Technol, Coll Informat Engn, Haikou 571126, Hainan, Peoples R China
[2] Univ Malaysia Sabah, Fak Komp Dan Informat, Sabah, Malaysia
来源
INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS | 2024年 / 18卷 / 04期
基金
海南省自然科学基金;
关键词
STD processing; picture data compaction; high-performance image compaction algorithms; compaction rate; JPEG compaction algorithm; COMPRESSION;
D O I
10.3233/IDT-230234
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the passage of time, the importance of spatio-temporal data (STD) is increasing day by day, but the spatiotemporal characteristics of STD bring huge challenges to data processing. Aiming at the problems of image information loss, limited compression ratio, slow compression speed and low compression efficiency, this method based on image compression. This article intended to focus on aircraft trajectory data, meteorological data, and remote sensing image data as the main research objects. The research results would provide more accurate and effective data support for research in related fields. The image compaction algorithm based on deep learning in this article consisted of two parts: encoder and decoder, and this method was compared with the JPEG (Joint Photographic Experts Group) method. When compressing meteorological data, the algorithm proposed in this paper can achieve a maximum compaction rate of 0.400, while the maximum compaction rate of the JPEG compaction algorithm was only 0.322. If a set of aircraft trajectory data containing 100 data points is compressed to 2:1, the storage space required for the algorithm in this paper is 4.2 MB, while the storage space required for the lossless compression algorithm is 5.6 MB, which increases the compression space by 33.33%. This article adopted an image compaction algorithm based on deep learning and data preprocessing, which can significantly improve the speed and quality of image compaction while maintaining the same compaction rate, and effectively compress spatial and temporal dimensional data.
引用
收藏
页码:2885 / 2899
页数:15
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