Multimedia Data Processing Technology and Application Based on Deep Learning

Author:

Li Guo1ORCID,Liu Wei1

Affiliation:

1. College of Machanical and Electrical Engineering, Nanyang Normal University, Nanyang, Henan 473000, China

Abstract

With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way.

Funder

Foundation of Excellent Young-Backbone Teacher of Colleges and Universities in Henan Province

Publisher

Hindawi Limited

Subject

General Computer Science

Reference15 articles.

1. Collaborative deep learning for recommender systems;H. Wang

2. Overview of deep learning;Z. J. Sun;Application Research of Computers,2021

3. Deep learning for efficient discriminative parsing;R. Collobert

4. Deep learning of representations for unsupervised and transfer learning;Y. Bengio;Workshop on Unsupervised and Transfer Learning,2021

5. A guide to convolution arithmetic for deep learning;V. Dumoulin,2020

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Promotion Strategy of Multimedia Network Teaching Platform in College Physical Education Teaching;International Journal of e-Collaboration;2024-05-31

2. The changing roles of mass media amidst the growth of the digital media;Cogent Social Sciences;2024-01

3. Retracted: Multimedia Data Processing Technology and Application Based on Deep Learning;Advances in Multimedia;2023-12-13

4. Design of Data Information Technology Optimization based on Deep Learning;2023 International Conference on Integrated Intelligence and Communication Systems (ICIICS);2023-11-24

5. Role of NLP and Deep Learning for Multimedia Data Processing and Security;Recent Advancements in Multimedia Data Processing and Security;2023-09-28

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3