LLM-Driven Heuristic Frame-Level Quantization Parameter Adaptation for VVenC
For video encoding practitioners, this work demonstrates a novel LLM-driven approach to automatically discover RDO heuristics, though the improvements are incremental over existing methods.
The paper addresses frame-level QP allocation in video encoding by using LLMs to automatically design RDO heuristics. The evolved heuristic achieves rate-distortion improvements over fixed-QP and Lagrangian baselines across multiple test sets.
Optimal frame-level quantization parameter (QP) allocation remains a persistent challenge in modern video encoders. The fixed-QP scheme widely adopted in practical systems is inherently content-agnostic, while classical Lagrangian rate-distortion optimization (RDO) methods often suffer from inaccurate multiplier settings. In this paper, we explore the use of large language models (LLMs) to automatically design RDO heuristics for frame-level QP adaptation. We construct a closed-loop evolutionary framework in which the LLM iteratively proposes RDO heuristics as algorithmic ideas with executable code, and these candidates are evaluated directly through encoding with the Fraunhofer Versatile Video Encoder (VVenC), where each heuristic acts as a scoring function that compares different QP choices based on the encoding statistics of past frames and current candidates. Experimental results across multiple test sets show that the evolved heuristic achieves promising rate-distortion improvements over both the fixed-QP scheme and the Lagrangian baseline. Further analysis reveals that the LLM can autonomously discover an adaptive heuristic that penalizes QP fluctuations via entropy-based terms, providing new insights into the design of RDO algorithms