Automatic Video Quality Assessment and Optimization of MOOC Platform Using ResNet

This study aims to explore how to use ResNet (deep residual network) to realize the automatic evaluation and optimization of video quality in the MOOC (large-scale open online course) platform. As a key component of modern education, the MOOC platform faces the challenge of ensuring high-quality video content. Manual evaluating and optimizing video quality is time-consuming and influenced by subjective factors, limiting the improvement of educational effects. This study based on ResNet explores its application in video frame analysis, video quality assessment metrics and methods, and automated optimization strategies. Specifically, we investigate the potential applications of ResNet for video clarity, fluency, stability, anomaly detection, and automatic parameter adjustment and propose corresponding automatic optimization methods. Through experimental validation, we show the effectiveness of ResNet in MOOC video quality assessment and optimization. This study offers new possibilities to improve the quality and effectiveness of online education.