用视频复杂度分析器预测每帧比特数，提升编码效率
Content-Driven Frame-Level Bit Prediction for Rate Control in Versatile Video Coding
- 基于VCA轻量特征与量化参数，用随机森林预测帧级比特
- 对I/P/B帧的预测相关性高达0.93/0.88/0.77，接近真实值
- 编码时间减少33.3%，适合实时视频编码场景
率控制在维多利亚视频编码（VVC）中通过合理分配比特以满足目标码率并保持质量。传统两遍率控制（2pRC）依赖解析率-量化参数（QP）模型，难以捕捉非线性时空变化，导致画质不稳定且因多次试编码而复杂度高。本文提出内容自适应框架，利用视频复杂度分析器（VCA）的轻量特征与量化参数，在随机森林回归中预测帧级比特消耗。在使用VVenC编码的超高清序列上，该模型对I、P、B帧的预测与真实值相关性分别为0.93、0.88和0.77。集成至率控环后，编码效率媲美2pRC，总编码时间减少33.3%。结果表明，基于VCA的比特预测是传统率-QP模型的高效准确替代方案。
原文摘要 · Abstract (English)
Rate control allocates bits efficiently across frames to meet a target bitrate while maintaining quality. Conventional two-pass rate control (2pRC) in Versatile Video Coding (VVC) relies on analytical rate-QP models, which often fail to capture nonlinear spatial-temporal variations, causing quality instability and high complexity due to multiple trial encodes. This paper proposes a content-adaptive framework that predicts frame-level bit consumption using lightweight features from the Video Complexity Analyzer (VCA) and quantization parameters within a Random Forest regression. On ultra-high-definition sequences encoded with VVenC, the model achieves strong correlation with ground truth, yielding R2 values of 0.93, 0.88, and 0.77 for I-, P-, and B-frames, respectively. Integrated into a rate-control loop, it achieves comparable coding efficiency to 2pRC while reducing total encoding time by 33.3%. The results show that VCA-driven bit prediction provides a computationally efficient and accurate alternative to conventional rate-QP models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。