Machine Learning-Driven Pre-Broadcast Video Codec Validation: Ensuring Seamless Television Transmission - El Fayq, Khalid; Tkatek, Said; Idouglid, Lahcen (IAES Indonesian Section, 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.