Better Video Quality through Deep Learning

Per-title encoding, also known as content-adaptive encoding, is a technique that adjusts video quality based on the type of content. This approach can lead to significant improvements in video quality without increasing bandwidth usage. Mux has developed an instant per-title encoding method using deep learning models, which allows for near Netflix-level results with minimal additional cost and processing time. The process involves training a neural network on millions of videos across various content types and creating feature embeddings for each video ingested into the system. This enables the model to output optimal encoding ladders in just seconds, significantly reducing the time required compared to traditional methods. Initial results have shown improvements in quality as measured by VMAF (Video Multimethod Assessment Fusion), with some clips showing differences of up to 12 VMAF points. As the training set grows over time, the accuracy of the model is expected to improve further, leading to even better video quality for end-users at no additional cost.