GAN-IoTVS: A Novel Internet of Multimedia Things-Enabled Video Streaming Compression Model Using GAN and Fuzzy Logic
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Le résumé fourni par la source
This study proposes a novel method for streaming data compression and encoding protocols based on generative adversarial networks (GANs) and fuzzy logic. The concept of GAN and fuzzy logic integration creates a different prospect as compared to the existing multimedia streaming data compression and security protocols. This article proposed a collaborative model for multimedia data compression by integrating GAN with fuzzy logic. It guarantees data compression using a secure channel for the verification platform of multimedia-enabled videos. Thus, it defines a fuzzy-based weight function for controlling the frequency of the current objects by the contents that effectively handle a string of code generation, which is designed and developed. It also eliminates adversarial loss removes streaming data redundancy and saves resource constraints using static–dynamic memory, transmission bandwidth, and computational power. In addition, this proposed model reduces the number of lossless video files before encryption by increasing the entropy of the sequence of coded images. Fuzzy logic code provides a more secure multimedia information mapping on the code contents. Substantially, the private key is generated when multimedia-enabled video files are embedded and sent to the receiver, and decoding information is possible using the same key. To illustrate the simulation of the theoretical results and model experiments are performed under the Python 3.9 tool. The experimental results show that a better compression ratio is achieved compared with the other state-of-the-art models. There is no difference between the original video and the compression after decoding, and the rate is increased to over 30.13%.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- GAN-IoTVS: A Novel Internet of Multimedia Things-Enabled Video Streaming Compression Model Using GAN and Fuzzy Logic
- Date Crossref
- 01/12/2023
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Sindh Madressatul Islam University Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Shaheed Benazir Bhutto University pays non établi dans la noticeUniversité ou école supérieure
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Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Shenyang Normal University Software Collage pays non établi dans la noticeUniversité ou école supérieure
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Princess Nourah bint Abdulrahman University pays non établi dans la noticeUniversité ou école supérieure
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Taif University pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Sciences Department of Information Technology pays non établi dans la noticeUniversité ou école supérieure
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College of Computers and Information Technology Department of Information Technology pays non établi dans la noticeUniversité ou école supérieure
Department of Computer Science — Sindh Madressatul Islam University, Shaheed Benazir Bhutto University et Department of Computer Science and Information Technology — Benazir Bhutto Shaheed University Lyari, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.