Research on Video Quality Diagnosis System Based on Convolutional
Neural Network

Research on Video Quality Diagnosis System Based on Convolutional
Neural Network

Convolutional Neural Network, Video Quality Diagnosis System, Internet

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.

Yi Hu 1, Xiaodong Zhan

2023

peri irawan

pdf

englist

text