A methodology, metric, and use case for fast encoding screening strategies in video coding
Year: 2026
Type: article
Abstract: The growing complexity of video encoding standards, particularly Versatile Video Coding (VVC), requires fast encoding techniques, often based on machine learning (ML), to enable practical encoder and decoder implementation. Traditional evaluation methods demand extensive computational resources, often resulting in hundreds of hours of encoding time, especially when assessing many models. This article proposes a novel methodology to systematically evaluate and compare early-stage ML-based approaches for accelerating video encoders, enabling assessments in reasonable timeframes while ensuring consistent comparisons. Additionally, the BD-rate-Time Reduction Ratio (BTR) metric is introduced to compare acceleration algorithms with a single value. A case study applies several ML classifiers to accelerate the first-level quadtree splitting in VVC, achieving a 20.18% time reduction with only a 0.96% BD-rate increase. Results show that the methodology supports large-scale model evaluation and improves the reliability of comparisons for future advances in video encoding efficiency.
Source: PeerJ Computer Science
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,395
Awards: G430777504, G6516743866
OpenAlex ID: W7165138113