short-paper

Improving Netflix Video Quality with Neural Networks

Authors:

Christos Bampis,

Li-Heng Chen,

Zhi LiAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Page 760

https://doi.org/10.1145/3543873.3587553

Published: 30 April 2023 Publication History

Get Access

Abstract

Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input video is downscaled into multiple lower-resolution videos. This is typically done by a conventional resampling filter like Lanczos. In this talk, we describe how we improved Netflix video quality by developing neural networks for video downscaling and deploying them at scale.

References

[1]

Li-Heng Chen, Christos G. Bampis, Zhi Li, Chao Chen, and Alan C. Bovik. 2022. Convolutional block design for learned fractional downsampling. In Proceedings of the 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 640–644.

Crossref

Google Scholar

[2]

Li-Heng Chen, Christos G. Bampis, Zhi Li, Joel Sole, and Alan C. Bovik. 2021. A progressive architecture for learned fractional downsampling. In Proceedings of 2021 Picture Coding Symposium (PCS). IEEE, 46–50.

Crossref

Google Scholar

[3]

Zhi Li, Anne Aaron, Ioannis Katsavounidis, Anush Moorthy, and Megha Manohara. 2016. Toward A Practical Perceptual Video Quality Metric. Retrieved March 14, 2023 from https://netflixtechblog.com/toward-a-practical-perceptual-video-quality-metric-653f208b9652

Google Scholar

[4]

Zhi Li, Christos Bampis, Julie Novak, Anne Aaron, Kyle Swanson, Anush Moorthy, and Jan De Cock. 2018. VMAF: The Journey Continues. Retrieved March 14, 2023 from https://netflixtechblog.com/vmaf-the-journey-continues-44b51ee9ed12

Google Scholar

[5]

Frank San Miguel. 2021. The Netflix Cosmos Platform. Retrieved March 14, 2023 from https://netflixtechblog.com/the-netflix-cosmos-platform-35c14d9351ad

Google Scholar

Index Terms

Improving Netflix Video Quality with Neural Networks
1. Computing methodologies
  1. Computer graphics
    1. Image compression
  2. Machine learning
2. Networks
  1. Network services
    1. Cloud computing

Recommendations

Improving Netflix video quality with neural networks
MHV '23: Proceedings of the 2nd Mile-High Video Conference

Video downscaling is an important component of adaptive video streaming, which tailors streaming to screen resolutions of different devices and optimizes picture quality under varying network conditions. With video downscaling, a high-resolution input ...
No-reference video quality measurement using neural networks
DSP'09: Proceedings of the 16th international conference on Digital Signal Processing

Objective video quality measurements emerge as an important issue as multimedia data is increasingly transmitted over the channels where bandwidth may not be guaranteed. Among various objective models for video quality measurement, no-reference models ...
Can You See What I See? Quality-of-Experience Measurements of Mobile Live Video Broadcasting

Broadcasting live video directly from mobile devices is rapidly gaining popularity with applications like Periscope and Facebook Live. The quality of experience (QoE) provided by these services comprises many factors, such as quality of transmitted ...

Comments

Information & Contributors

Information

Published In

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

WWW '23

Sponsor:

SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
142
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)4

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Abstract

References

Index Terms

Recommendations