Machine-learning based VMAF prediction for HDR video content

被引：2

作者：

Mueller, Christoph ^{[1
]}

Steglich, Stephan ^{[1
]}

Gross, Sandra ^{[2
]}

Kremer, Paul ^{[2
]}

机构：

[1] Fraunhofer FOKUS, Berlin, Germany

[2] RTL Technol, Cologne, Germany

来源：

PROCEEDINGS OF THE 2023 PROCEEDINGS OF THE 14TH ACM MULTIMEDIA SYSTEMS CONFERENCE, MMSYS 2023 | 2023年

关键词：

VMAF; video quality; HDR; neural networks; machine learning;

D O I：

10.1145/3587819.3593941

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a methodology for predicting VMAF video quality scores for high dynamic range (HDR) video content using machine learning. To train the ML model, we are collecting a dataset of HDR and converted SDR video clips, as well as their corresponding objective video quality scores, specifically the Video Multimethod Assessment Fusion (VMAF) values. A 3D convolutional neural network (3D-CNN) model is being trained on the collected dataset. Finally, a hands-on demonstrator is developed to showcase the newly predicted HDR-VMAF metric in comparison to VMAF and other metric values for SDR content, and to conduct further validation with user testing.

引用

页码：328 / 332

页数：5

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