AI in encoding is the umbrella term for using machine learning to make video encoding smarter. The category covers several distinct applications. ML-driven encoder decisions train models to predict the best mode choice, block partition or quantization for each region of a frame, replacing hand-tuned heuristics that took decades to develop with learned ones that can outperform them. Perceptual quality optimisation uses ML models (often related to VMAF) to guide encoder decisions toward what humans actually notice rather than what minimises raw pixel error. Content classification uses scene-understanding models to tag content type (animation, sports, talking head, screen recording) and pick encoding strategies per class.
The category is distinct from neural-codec — entirely AI-built codec replacements like end-to-end learned compression. AI-in-encoding lives inside or alongside traditional codecs (H.264, HEVC, AV1, VVC), making them work better without breaking compatibility. Every viewer decodes the same standard bitstream with their existing hardware — only the encoder got smarter. That backwards compatibility makes AI-in-encoding far more deployable than neural codecs in 2026.
For a product team, AI-in-encoding is already in many commercial pipelines without being prominently marketed. Netflix uses ML for shot detection, complexity analysis and quality estimation. YouTube uses learned models for bitrate ladder selection. NETINT VPUs include built-in ML-based perceptual encoding (their CAE feature). Bitmovin and Mux ship AI-based per-title and per-scene optimisation. The typical benefit: 10–25 % bitrate savings at the same VMAF, on top of whatever traditional encoder you're using. The decision is rarely "should we use AI in encoding" but "which cloud encoding service do we buy that already does this".
