A Hardware-Friendly Acceleration of VVC Affine Motion Estimation Using Decision Trees

被引：0

作者：

Viana, Ramiro ^{[1
]}

Loose, Marta ^{[1
,2
]}

Ferreira, Rafael ^{[3
]}

Porto, Marcelo ^{[1
]}

Correa, Guilherme ^{[1
]}

Agostini, Luciano ^{[1
]}

机构：

[1] ViTech UFPel, PPGC, Pelotas, RS, Brazil

[2] IFFar, Pelotas, RS, Brazil

[3] ViTech UFPel, Pelotas, RS, Brazil

来源：

2024 37TH SBC/SBMICRO/IEEE SYMPOSIUM ON INTEGRATED CIRCUITS AND SYSTEMS DESIGN, SBCCI 2024 | 2024年

关键词：

Affine Motion Estimation; Versatile Video Coding; Machine Learning; Decision Trees; Hardware-Friendly;

D O I：

10.1109/SBCCI62366.2024.10703987

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Digital videos have become ubiquitous across various devices, presenting challenges in transmitting and storing due to their substantial data volume. Video encoders play a vital role in addressing these challenges by improving data compression. The Versatile Video Coding (VVC) encoder, designed to enhance encoding processes compared to its predecessors, introduces a novel tool for Inter-Frame Prediction called Affine Motion Estimation (AME). This paper focuses on the Affine tool inside the Inter-Frame Prediction step, with the aim of developing an optimized, hardware-friendly algorithm. The goal is to reduce computational effort while maintaining the coding efficiency of the VVC encoder. To achieve this, Machine Learning models based on Decision Trees, known for their simplicity in implementation, are employed. The proposed solution successfully achieves an average time reduction of 5.07% in total encoding time, with a minimal coding efficiency loss of 0.23%.

引用

页码：205 / 209

页数：5