Disney patents AI method to improve video compression quality
Disney has filed a patent for a system that uses a trained neural network to decide how much to compress each frame of a video. The model learns from past data to balance keeping the file size small with maintaining high visual quality during streaming.
In the filing’s own words
A system and method for video encoder rate control that leverages offline-trained sequence modeling neural networks to dynamically select quantization parameters for video encoding, ensuring adherence to target bitrate constraints while optimizing video quality. Training data comprising sequences of encoder states, quantization parameter decisions, and bitrate usage is augmented using sparse Gaussian Processes and Fourier transform-based techniques to create diverse encoding trajectories. The neural network, preferably a transformer-based architecture, is trained to predict optimal quantization parameter sequences, incorporating a trajectory stitching mechanism for enhanced policy generalization. During deployment, the trained model adaptively predicts quantization parameters for each frame or coding unit, enabling efficient video compression and streaming across diverse content types and network conditions. Embodiments provide robust, practical solutions for media processing pipelines, content delivery networks, and streaming platforms, and methods for both training and deploying the model are disclosed.
What exactly they claim (claim 1)
1. A method for controlling quantization parameter selection in video encoding, the method comprising: collecting training data comprising a plurality of trajectories, wherein each trajectory comprises a sequence of encoder states, quantization parameter (QP) decisions, returns-to-go indicative of future video quality, and costs-to-go indicative of a future bitrate budget; training, in an offline and return-conditioned manner, a sequence modeling neural network using the training data to predict a sequence of QP decisions that satisfies a target bitrate constraint while maximizing video quality; and deploying the trained sequence modeling neural network to encode a video sequence by, for each of one or more frames of the video sequence: inputting a sequence of prior encoder states, QP decisions, returns-to-go, and costs-to-go into the trained sequence modeling neural network; obtaining, from the trained sequence modeling neural network, a predicted QP decision for a current frame; and encoding the current frame using the predicted QP decision.
Facts
- Company
- Disney (Disney Enterprises, Inc.)
- Official title
- OFFLINE REINFORCEMENT LEARNING FOR VIDEO ENCODER RATE CONTROL
- Publication number
- US20260292200A1
- Application number
- 19550143
- Filed
- Feb 25, 2026
- Published
- Sep 24, 2026
- Inventors
- 4
- Claims
- 20
Classified under
H04N 19/149 Electricity & electronicsG06N 3/0455 Physics & computingG06N 3/08 Physics & computingH04N 19/172 Electricity & electronicsMore from Disney in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.