Audio and video editing system design based on OpenCV

Author:

Song Yuehang,Chen Borun,Liu Xiaobin,Weijun Hu,Xiangyu Xie,Yuqi Yan

Abstract

With the rapid development of the Internet, a new carrier for people to perceive the world and communicate with each other - audio and video - is gradually being favoured by the public. The development of multimedia technology and artificial intelligence technology has provided a milestone for the maturity of audio and video technology. In particular, short video platforms have slowly become a new network position for various media promotions. Especially at the moment of the epidemic, the channel of understanding the world through audio and video is increasingly valued. The public has put forward higher demands on the content and presentation of audio and video. Therefore, it is particularly important to produce quality audio-video that meets the requirements of the times, which cannot be achieved without a feasible audio-video editing system. In addition, after previous research and practice, the application of artificial intelligence technology in the field of imaging has also become mature, including some applications in the direction of entertainment. Applying AI technology to the video editing process can improve the efficiency of video editing, increase the interest of video content, and allow video creators to focus on content creation without spending too much time and energy on video editing operations, thus creating better quality videos. This design is the main technology of OpenCV and front-end technology stack, such as JavaScript, React and Electron, to implement basic video editing, video filters, in addition to the development of a friendly interactive interface. The implementation of basic video editing module and video filter module are both based on OpenCV implementation. In this design, the basic video editing implements pan, zoom and rotate operations on the video, and the video filter module is implemented by changing the RGB channel values of the image. The operations on the video can be broken down into operations on each frame of the video, and OpenCV provides a way to implement these operations. The paper concludes with a summary of the shortcomings and flaws in the design, and an outlook on the next steps and future directions.      This design is the main technology of OpenCV and front-end technology stack, such as JavaScript, React and Electron, to implement basic video editing, video filters, in addition to the development of a friendly interactive interface. The implementation of basic video editing module and video filter module are both based on OpenCV implementation. In this design, the basic video editing implements pan, zoom and rotate operations on the video, and the video filter module is implemented by changing the RGB channel values of the image. The operations on the video can be broken down into operations on each frame of the video, and OpenCV provides a way to implement these operations.     The paper concludes with a summary of the shortcomings and flaws in the design, and an outlook on the next steps and future directions.    

Publisher

Krasnoyarsk Science and Technology City Hall

Reference26 articles.

1. Wang Xiaohong, Bao Yuanyuan, Lv Qiang. Development Status and Trend Observation of Mobile Short Video. China Editor. 2015; 03:7-12.

2. J. Wu, P. P. C. Lee, Q. Li, L. Pan and J. Zhang, CellPAD: Detecting Performance Anomalies in Cellular Networks via Regression Analysis. 2018 IFIP Networking Conference (IFIP Networking) and Workshops. 2018; 1-9.

3. Zeng Runxi, Mo Minli. A Study on the Differences and Influencing Factors of Short Video Multi-Platform Communication Effects. Journal of Guangxi Normal University (Philosophy and Social Sciences Edition). 2022; 58(01):133-144. DOI: 10.16088/j.issn.1001-6597.2022.01.012

4. C. Yuan, X. Liu and Z. Zhang, The Current Status and progress of Adversarial Examples Attacks. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE). 2021; 707-711.

5. Zhou Feiyan, Jin Linpeng, Dong Jun. A Review of Convolutional Neural Networks. Chinese Journal of Computers. 2017; 40(06): 1229-1251.

Cited by 4 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. DETR-crowd is all you need;Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies;2023-05-30

2. Review: the application of artificial intelligence in distribution network engineering field;Информатика. Экономика. Управление - Informatics. Economics. Management;2023-03-20

3. Research on computer vision application in industry field: focus on distribution network engineering;Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies;2023-03-20

4. Gamification of E-Learning Based on Information Technology;Networks and Systems in Cybernetics;2023

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