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RUNTIME TEMOTE VIDEO QUALITY ASSESSMENT
Runtime Remote Video Quality Assessment is a real-time video analysis system designed to evaluate the quality of video streams without requiring the original reference video. With the rapid growth of applications such as video conferencing, online education, telemedicine, live streaming, and remote surveillance, maintaining high video quality has become essential for ensuring a better Quality of Experience. However, factors such as network congestion, bandwidth limitations, packet loss, compression artifacts, and environmental conditions can significantly degrade video quality during transmission. The proposed RVQA system uses computer vision and machine learning techniques to analyze video quality automatically. The system accepts a video as input, extracts frames using OpenCV, preprocesses the frames, and computes important visual features such as brightness, contrast, sharpness, blur, and noise. These extracted features are then processed by a machine learning model to estimate the overall video quality and classify it into categories such as Excellent, Good, Fair, or Poor. A webbased interface developed using Flask allows users to upload videos, perform real-time quality analysis, and view the assessment results through graphs and quality metrics. The system is implemented using Python, Flask, OpenCV, NumPy, Matplotlib, and Scikit-learn, providing a lightweight, scalable, and user-friendly solution for runtime video quality monitoring. The proposed system helps detect quality degradation efficiently and supports applications including multimedia streaming, online learning, video conferencing, telemedicine, and intelligent surveillance. Overall, the Runtime Remote Video Quality Assessment system offers an effective approach for improving video quality monitoring and enhancing the overall user experience in modern multimedia communication environments.
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