Search papers, labs, and topics across Lattice.
This paper introduces a feedback-driven rate control framework for learned video compression that addresses the challenges of stable bitrate tracking in dynamic video content. By implementing a single-model multi-rate coding interface and a log-domain PI/PID closed-loop controller, the method continuously adjusts the rate-distortion parameter lambda based on real-time bitrate errors. Experimental results demonstrate significant improvements, achieving average bitrate errors as low as 2.13% and reducing BD-rates by up to 5.69%, highlighting the framework's effectiveness in budget-constrained scenarios.
Achieving stable bitrate tracking in learned video compression can reduce average bitrate errors to as low as 2.13%, transforming how we manage video quality under constraints.
End-to-end learned video compression has achieved strong rate-distortion performance, but rate control remains underexplored, especially in target-bitrate-driven and budget-constrained scenarios. Existing methods mainly rely on explicit R-D-lambda modeling or feed-forward prediction, which may lack stable online adjustment when video content varies dynamically. We propose a feedback-driven rate control framework for learned video compression. First, we build a single-model multi-rate coding interface on top of a DCVC-style framework, enabling continuous bitrate control through the rate-distortion parameter lambda. Then, a log-domain PI/PID closed-loop controller updates lambda online according to the error between the target bitrate and the entropy-estimated bitrate, achieving stable target bitrate tracking. To further improve frame-level bit allocation under budget constraints, we introduce a dual-branch GRU-based adjustment controller that refines the base control signal using budget-state features and causal coding statistics. Experiments on UVG and HEVC show that the proposed PI/PID controller achieves average bitrate errors of 2.88% and 2.95% on DCVC and DCVC-TCM, respectively. With the proposed adjustment controller, the method further achieves average BD-rate reductions of 5.69% and 4.49%, while reducing the average bitrate errors to 2.13% and 2.24%. These results show that the proposed method provides a practical solution for learned video compression with both controllable bitrate and improved rate-distortion performance.
