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Laboratory for Image & Video Engineering

Welcome to the LIVE Netflix Video Quality of Experience Database

LIVE Netflix Video Quality of Experience Database

Introduction

Given the exploding use of mobile video devices and the large network bandwidth demand by streaming users, the biggest challenges in video content delivery are to create better network-aware strategies to improve end-users quality of experience (QoE). In this direction, HTTP Adaptive Streaming (HAS) is being used by content providers as a way of dealing with network fluctuations. We conducted experiments to gather subjective data that will help us understand the effect of such client-based strategies on QoE. The newly created LIVE-Netflix Video Quality of Experience database consists of 112 distorted videos evaluated by over 55 human subjects on a mobile device. We hope that the database will be useful in designing general QoE-aware objective prediction models and developing tools to create perceptually optimized network allocation protocols.

Download

We are making LIVE Netflix Video Quality of Experience Database available to the research community free of charge. If you use this database in your research, we kindly ask that you to cite our paper listed below:

  • C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron, and A. C. Bovik, “Temporal Effects on Subjective Video Quality of Experience,” in preparation.
  • C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron and A. C. Bovik, "LIVE Netflix Video Quality of Experience Database," Online: http://live.ece.utexas.edu/research/LIVE_NFLXStudy/index.html, 2016.

You can download the publicly available release of the database by clicking THIS link. Please fill THIS FORM and the password will be sent to you.

Database Description

The goal of our study is to understand the influence of mixtures of dynamic network impairments such as rebuffing events and compression on Quality of Experience of users watching videos on mobile devices. The LIVE-Netflix Video Quality of Experience database consists of 112 distorted videos evaluated by over 55 human subjects on a mobile device. The distorted videos were generated from 14 video contents of spatial resolution 1080p at 24, 25 and 30 fps by imposing a set of 8 different playout patterns on them ranging from dynamically changing H.264 compression rates and re-buffering events to a mixture of compression and re-buffering. The database contains 11 different types of content provided by Netflix (drama, action, comedy, anime etc.) and 3 publicly available video contents from the Consumer Digital Video Library (CDVL). The Mean Opinion Scores (MOS) obtained from the subjective evaluations and metadata for all the videos in the database, are also made available.

Investigators

The investigators in this research are:

Copyright Notice

-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------
Copyright (c) 2016 The University of Texas at Austin
All rights reserved.

Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirety appear in all copies of this database, and the original source of this database, Laboratory for Image and Video Engineering (LIVE, http://live.ece.utexas.edu ) at the University of Texas at Austin (UT Austin, http://www.utexas.edu ), is acknowledged in any publication that reports research using this database.

The following papers are to be cited in the bibliography whenever the database is used as:

  • C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron, and A. C. Bovik, “Temporal Effects on Subjective Video Quality of Experience,” in preparation.
  • C. G. Bampis, Z. Li, A. K. Moorthy, I. Katsavounidis, A. Aaron and A. C. Bovik, "LIVE Netflix Video Quality of Experience Database," Online: http://live.ece.utexas.edu/research/LIVE_NFLXStudy/index.html, 2016.

IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

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