Reducing complexity in versatile video coding intra-coding through machine learning-based optimization of partitioning and prediction

The escalating demand for high-resolution multimedia content has necessitated more efficient video compression solutions. The Versatile Video Coding (VVC) standard, despite achieving remarkable compression gains, introduces significant computational complexity, primarily due to its exhaustive Rate-Distortion Optimization (RDO) process. To address this, we propose an intelligent approach leveraging supervised machine learning techniques to streamline the VVC encoding process. Specifically, we introduce a Lightweight Neural Network (LNN) for efficient coding unit partitioning decisions and a Decision Tree (DT) classifier for optimizing the intra prediction process. This dual-method framework, tailored for All Intra coding configuration, significantly reduces encoder complexity while maintaining compression performance and visual quality. Through extensive testing, we demonstrate a remarkable 65.47\% reduction in encoding time with minimal impact on compression efficiency and no perceptible degradation in video quality. These findings represent a significant step towards making high-efficiency VVC encoding more practical for real-world applications.