Leveraging Machine Learning for Enhanced Video Quality in E-Learning

In the rapidly evolving landscape of online education, video content has become an indispensable tool for effective knowledge dissemination. However, ensuring high- quality video content remains a challenge due to various factors such as bandwidth limitations, device diversity, and encoding constraints. This paper proposes a Multi-Frame Super- Resolution approach to address this challenge by leveraging machine learning techniques to enhance video quality in e-learning environments.Traditional video enhancement techniques often rely on predefined filters or manual adjustments, which can lead to inconsistent results and significant time investments. In contrast, the proposed approach utilizes machine learning algorithms to automatically analyze and improve video quality. By training on a diverse dataset of e- learning videos, the model learns to identify and correct common issues such as compression artifacts, low resolution, and visual noise.The proposed machine learning- based approach offers a scalable and automated solution to enhance video quality in e-learning without requiring manual intervention for each video. As online education continues to grow, the integration of such