Research on Video Quality Diagnosis System Based on Convolutional Neural Network

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.