Research on Video Quality Diagnosis System Based on Convolutional Neural Network

Yi Hu, Xiaodong Zhan


In the era of rapid development in modern society, there is an escalating demand for high-performance products. However, this quest for excellence often encounters persistent quality issues during practical applications. Hence, to enhance the user experience and rectify this situation, this paper proposes a Convolutional Neural Network (CNN)-based Video Quality Diagnosis System. The system's design encompasses a myriad of construction methodologies, primary framework structures, and associated databases. This research primarily focuses on video quality during video conferencing as the subject of investigation, with the aim of constructing a Video Quality Diagnosis System grounded in CNN theory. The objective is to provide real-time identification, analysis, and enhancement of video quality, thereby offering timely solutions to issues that arise in the video conferencing experience. In this endeavor, the research amalgamates cutting-edge technology and meticulous study to create a smoother and more immersive video conferencing experience for individuals and organizations. By addressing the frequently encountered video quality issues, we hope to facilitate more effective and engaging communication on a global scale, bridging the gap between user expectations and practical implementation and paving the way for a future where video quality problems are a thing of the past.

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Convolutional Neural Network; Video Quality Diagnosis System; Internet

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IJIIS: International Journal of Informatics and Information Systems

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Departement of Information System, Universitas Amikom Purwokerto, IndonesiaFaculty of Computing and Information Science, Ain Shams University, Cairo, Egypt
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