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.

翻译：码率控制通过在各帧间高效分配比特以满足目标码率，同时保持视频质量。通用视频编码中的传统两遍码率控制依赖于解析型码率-量化参数模型，这些模型往往难以捕捉非线性的时空变化，导致质量不稳定，且因多次尝试编码而产生高计算复杂度。本文提出一种内容自适应框架，利用视频复杂度分析器的轻量级特征和量化参数，通过随机森林回归预测帧级比特消耗。在使用VVenC编码的超高清序列上，该模型与真实值表现出强相关性，对I帧、P帧和B帧分别获得0.93、0.88和0.77的R²值。将其集成至码率控制环路后，可实现与传统两遍码率控制相当的编码效率，同时总编码时间减少33.3%。结果表明，基于VCA的比特预测为传统码率-量化参数模型提供了一种计算高效且精确的替代方案。