Lossless Compression Algorithm of Multimedia Data Based on Artificial Intelligence

Authors

  • Quanpeng Ji College of Artificial Intelligence, Chongqing University of Arts and Sciences, Yongchuan 402160, China

Abstract

In order to improve the storage efficiency of multimedia data in a multi-thread rule linked list, efficient data compression is needed. A lossless compression algorithm for multimedia data based on artificial intelligence is proposed. A similar cache structure model of multi-thread rule linked list multimedia data is constructed, a distributed cloud computing method is adopted to mine the autocorrelation characteristics of the multi-thread rule linked list multimedia data, high-dimensional statistical information of the multi-thread rule linked list multimedia data is extracted, a phase space reconstruction method is combined to reconstruct the characteristics of the multi-thread rule linked list multimedia data, a knowledge map technology is used to realize the integration and fusion of main components of the multi-thread rule linked list multimedia data, and an artificial intelligence learning method is adopted to realize the lossless compression optimization of the multimedia data. The simulation results show that the load of lossless compression of multimedia data in the multi-threaded rule linked list is large, the storage capacity is improved, and the real-time ability to access data is good. It has good application value in multi-threaded rule linked list multimedia data mining and storage structure optimization.

Keywords: Artificial Intelligence, Multimedia Data, Lossless Compression, Storage.

Cite As

Q. Ji, "Lossless Compression Algorithm of Multimedia Data Based on Artificial Intelligence",
Engineering Intelligent Systems, vol. 30 no. 1, pp. 23-33, 2022.


Downloads

Published

2022-01-01

Issue

Vol. 30 No. 1 (2022): International Journal of Engineering Intelligent Systems

Section

General Submission

License

The submission of a paper implies that, if accepted for publication, it will not be published elsewhere in the same form, in any language, without the prior consent of the publisher. Before publication, authors are requested to assign copyright to CRL Publishing Ltd. This allows CRL to sanction photocopying, and to authorize the reprinting of issues or volumes according to demand. Authors' traditional rights will not be jeopardized by assigning Copyright in this way, as they retain the right to reuse the material following publication, and to veto third-party publication.