Content-Driven Frame-Level Bit Prediction for Rate Control in Versatile Video Coding

2026-02-05

AI Summary

This work addresses the limitations of conventional two-pass rate control in VVC, which relies on analytical rate–QP models that struggle to accurately capture spatiotemporal nonlinearities, leading to quality fluctuations and high computational overhead. To overcome this, the authors propose a content-adaptive frame-level bit prediction framework that integrates lightweight content features extracted by a Video Complexity Analyzer (VCA) with random forest regression to accurately predict bit consumption for I-, P-, and B-frames within the rate control loop. Evaluated on ultra-high-definition sequences, the method achieves R² values of 0.93, 0.88, and 0.77 for I-, P-, and B-frames, respectively, while reducing encoding time by 33.3% without compromising coding efficiency, thereby offering an effective replacement for traditional rate–QP models.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• rate control
• Versatile Video Coding
• bit prediction
• rate-QP model
• encoding complexity

Innovation

• content-adaptive rate control
• frame-level bit prediction
• Video Complexity Analyzer (VCA)
• Random Forest regression
• Versatile Video Coding (VVC)