All-intra rate control using low complexity video features for Versatile Video Coding

TL;DR

This paper introduces a low-complexity, machine learning-based first pass for all-intra rate control in VVC, significantly reducing encoding time with minimal impact on compression efficiency.

Contribution

It presents a novel two-pass rate control method using low-complexity video features and RF models, improving encoding speed in VVC with minimal BD-rate increase.

Findings

32% reduction in encoding time for faster preset

Only 2% BD-rate increase on average

Maintains similar rate control accuracy

Abstract

Versatile Video Coding (VVC) allows for large compression efficiency gains over its predecessor, High Efficiency Video Coding (HEVC). The added efficiency comes at the cost of increased runtime complexity, especially for encoding. It is thus highly relevant to explore all available runtime reduction options. This paper proposes a novel first pass for two-pass rate control in all-intra configuration, using low-complexity video analysis and a Random Forest (RF)-based machine learning model to derive the data required for driving the second pass. The proposed method is validated using VVenC, an open and optimized VVC encoder. Compared to the default two-pass rate control algorithm in VVenC, the proposed method achieves around 32% reduction in encoding time for the preset faster, while on average only causing 2% BD-rate increase and achieving similar rate control accuracy.