FastGW: A Machine Learning-Based Early Skip for the AV1 Global Warped Motion Compensation

The growing consumption of digital media, driven by technological advancements and exacerbated by the COVID-19 pandemic, has led to an increased demand for efficient video compression techniques. Among the various video encoders available, the AOMedia Video 1 (AV1) stands out since it was defined by the Alliance for Open Media (AOMedia), which is formed by big techs such as Google, Amazon, NetFlix, Meta, and Intel, among others. AV1 was launched in 2018 and it reaches high compression rates, especially for high-resolution videos. However, AV1 computational cost is significantly higher when compared to other current codecs. This paper is focused on one of the main novelties introduced by AV1: the Global Warped Motion Compensation (GWMC) tool. A computational effort reduction approach called Fast Global Warped (FastGW), using machine learning, is proposed to reduce the GWMC processing time. Then, a decision tree was trained to decide whether to skip the GWMC’s most computationally intensive step: the Refinement. This decision tree was implemented inside the AV1 encoder, resulting in an average time reduction of 23% at the GWMC, with a minimal impact on coding efficiency of 0.14% in BD-BR on average. To the best of the authors’ knowledge, this is the first work in the literature exploring machine learning to reduce the AV1 GWMC computational effort.

I. Introduction

The consumption of digital media is increasingly prevalent in people’s daily lives, thanks to technological advancements achieved over recent years. In addition to the natural growth in video consumption through streaming services, the COVID−19 pandemic played a significant role in its boost. According to [1], in just the first weeks of lockdown in 2020, the consumption of streaming services increased by 29.6%, and social media by 40.8%.