Learned Codec Bitrate Adaptation via Probability Model Signals

The patent implements dynamic adaptability by introducing adaptation signals that modify the probability model based on target bitrate and reconstruction quality requirements. The system transitions from a static standardized codec to a dynamic system where the probability model can be adapted in real-time through gradient descent optimization, allowing the same codec to efficiently handle multiple bitrates and quality levels without sacrificing compatibility.

The patent changes the parameters of the probability model by introducing adaptation signals that modify model behavior based on encoding conditions. These adaptation signals adjust parameters such as quantization steps, probability distributions, and neural network weights dynamically, enabling the codec to optimize performance across different bitrates and reconstruction qualities while maintaining the standardized codec framework.

The patent applies preliminary action by pre-training the neural network components and pre-computing adaptation signals for different bitrate scenarios. The adaptation signals are generated based on predetermined target bitrates and reconstruction qualities, allowing the system to quickly switch between modes without complex real-time calculations. This pre-computation approach reduces runtime complexity while maintaining high adaptability.

The adaptation signals serve as intermediaries between the standardized codec framework and the multiple bitrate requirements. Rather than directly modifying the entire codec structure, the adaptation signals act as a lightweight intermediary layer that adjusts the probability model parameters, simplifying the implementation complexity while achieving versatile bitrate adaptation.

The patent performs gradient descent optimization in advance during the training phase to achieve high precision in the probability model. The neural network weights and adaptation signal generation mechanisms are pre-optimized using gradient descent, ensuring manufacturing precision is achieved before deployment. This preliminary training eliminates the need for time-consuming optimization during actual encoding operations.

The patent employs periodic action by conducting gradient descent optimization at specific intervals during training rather than continuously. The training process uses periodic updates to the probability model parameters, allowing the system to achieve high precision through structured, periodic optimization cycles rather than continuous computation, thereby reducing overall training time while maintaining precision.