Netflix uses machine learning algorithms to optimize how each video in its catalog is encoded for streaming, analyzing each title frame by frame to determine the ideal video quality settings that minimize file size while preserving visual quality for every subscriber's device and network conditions.

Netflix pioneered 'per-title encoding' in 2015, applying machine learning to analyze the complexity of each video and generate a custom set of quality levels (the 'encoding ladder') for that specific title — rather than using a one-size-fits-all standard. A simple animated show can be encoded at a lower bitrate than a visually complex action film without visible quality loss. Netflix's research group has published on this work and on 'Dynamically Optimized' encoding, which further adjusts encoding on a shot-by-shot basis. The system operates entirely in Netflix's infrastructure before content reaches users.