Machine Learning-Driven Pre-Broadcast Video Codec Validation: Ensuring Seamless Television Transmission - El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2024)
This study addresses the critical challenge of ensuring uninterrupted television broadcasting by proactively detecting video codec errors, focusing on TV Laayoune, a prominent Moroccan channel. We developed a machine learningbased methodology that identifies incompatible codecs before they disrupt live broadcasts. The approach involves data collection from multiple sources, including TV Laayoune's archives, metadata extraction via FFmpeg, and a hybrid model that combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks. Integrated into the broadcasting pipeline, this model achieved a 95% accuracy rate, significantly enhancing broadcast reliability and operational efficiency. Additionally, we propose a user-friendly interface for real-time error detection, comprehensive workflow integration, and automated alerts. This innovative solution addresses common broadcast challenges, reducing operational risks and improving the viewer experience.