Task-Oriented Video Semantic Coding with Reinforcement Learning

A semantic mask generation component is introduced as an intermediary module that generates semantic masks from task descriptions and video content. This mask serves as a mediator between the task requirements and the video coding process, enabling task-oriented optimization without fundamentally redesigning the coding standard. The semantic mask guides the mode decision component to select appropriate coding modes that preserve task-relevant information.

The mode decision component dynamically selects coding modes based on real-time analysis of semantic masks and video content. Instead of using fixed coding parameters, the system adapts its coding strategy frame-by-frame or block-by-block according to the detected semantic information, allowing flexible integration of task-oriented metrics into the existing coding framework.

The system applies different coding qualities to different regions of the video based on semantic importance. Regions identified as task-relevant through semantic masks receive higher coding precision and more bits, while background or less important regions are compressed more aggressively. This localized quality adjustment preserves semantic accuracy where needed without unnecessarily increasing overall bandwidth consumption.

The coding parameters such as quantization step size, transform coefficients, and entropy coding rates are dynamically adjusted based on the semantic mask and task requirements. By changing these parameters adaptively rather than using fixed high-quality settings throughout, the system maintains semantic accuracy for important content while reducing bandwidth usage for less critical areas.

The reinforcement learning model is pre-trained offline using a large dataset of video content and task requirements. During actual video coding, the pre-trained model quickly predicts optimal coding modes without requiring complex real-time training computations. This preliminary action separates the heavy computational burden of model learning from the real-time coding process, reducing instantaneous computational complexity while maintaining optimization efficiency.

Instead of training a new reinforcement learning model for each video or task, the system uses a pre-trained model that has learned general patterns from extensive training data. This copied knowledge allows the system to make optimized mode decisions rapidly without re-computing learning results, significantly reducing real-time computational requirements while preserving coding optimization performance.