Affiliations: [a] College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou, Hainan, China | [b] Fakulti Komputeran Dan Informatik, Universiti Malaysia Sabah, Sabah, Malaysia
Correspondence:
[*]
Corresponding authors: Kongduo Xing, College of Information Engineering, Hainan Vocational University of Science and Technology, Haikou 571126, Hainan, China. E-mail: duoduo168898@126.com. Rayner Alfred, Fakulti Komputeran Dan Informatik, Universiti Malaysia Sabah, Sabah, Malaysia. E-mail: ralfred@ums.edu.my.
Abstract: 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.
