Offline reinforcement learning for video encoder rate control

A method for training a neural network for video encoder rate control, 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;
• augmenting the training data by perturbing QP decision sequences within the plurality of trajectories to generate additional, valid trajectories;
• and
• training, in an offline and return-conditioned manner, a sequence modeling neural network using the augmented training data to predict a sequence of QP decisions that optimizes a trade-off between video quality and bitrate usage based on the returns-to-go and costs-to-go.

A method for controlling quantization parameter selection in video encoding using a trained sequence modeling neural network, the method comprising:

• receiving a video sequence for encoding;
• and
• for each of one or more frames or coding units of the video sequence:
• constructing an input sequence for the trained sequence modeling neural network, the input sequence comprising a history of prior encoder states, QP decisions, target returns-to-go indicative of desired future video quality, and target costs-to-go indicative of a desired future bitrate budget;
• applying a trajectory composition mechanism to dynamically adjust a history length of the input sequence based on learned in-support return and cost values to generate an adjusted input sequence;
• obtaining, from the trained sequence modeling neural network, a predicted QP decision for a current frame or coding unit based on the adjusted input sequence;
• and
• encoding the current frame or coding unit using the predicted QP decision.