论文展示LLM能设计视频编码工具，比传统方法节省0.18%比特率，复杂度仅增加0.4%。
该研究探索大语言模型能否设计视频编码工具，以Planar模式为例进行实证研究。实验在生成-评估循环中进行，LLM生成新的Planar预测器，编码器评估其性能并提供反馈。在Fraunhofer Versatile Video Encoder(VVenC)的快速预设下，LLM生成的模式比传统Planar模式节省0.18%的比特率，复杂度仅增加0.4%。在增强压缩模型(ECM)评估中，两种集成策略在低分辨率约束下均能带来编码增益。
Can LLMs Design Video Coding Tools? A Case Study on Planar Mode
This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic coupling of tool modifications. In particular, we present an empirical case study on the Planar mode, a long-standing intra prediction tool in video coding standards. Our experiments operate within a generation-and-evaluation loop, with the LLM generating new Planar predictors, encoder trials evaluating their coding performance, and the LLM re-generating refined implementations based on the evaluation feedback. We first examine directly replacing the default Planar mode in the Fraunhofer Versatile Video Encoder (VVenC) under its faster preset. Experimental results demonstrate that the LLM-generated mode can outperform the conventional Planar mode on this lightweight toolset, achieving 0.18% bitrate savings with 0.4% complexity overhead on the standard benchmark. We further extend our evaluation to the Enhanced Compression Model (ECM). Leveraging newly introduced directional Planar modes, we investigate two integration strategies: directly replacing them, and introducing the LLM-generated predictor as an additional prediction mode with new syntax elements. The empirical results suggest that both strategies can yield coding gains under a constrained low-resolution setting. Overall, this study offers preliminary evidence and practical insights, highlighting both the potential and open challenges of LLM-based coding tool design.