AVT-Crowd360 - Mouse Movement Data from Subjective Video Quality Tests
This open-source dataset contains mouse movement data collected from three distinct subjective video quality tests, representing different testing environments: crowd-sourced, out-of-the-lab, and laboratory settings. The dataset is accompanied by an enhanced version of the AVrateVoyager framework, a web-based platform for conducting subjective quality assessment studies.
Description
This open-source dataset contains mouse movement data collected from three distinct subjective video quality tests, representing different testing environments: crowd-sourced, out-of-the-lab, and laboratory settings. The dataset is accompanied by an enhanced version of the AVrateVoyager framework, a web-based platform for conducting subjective quality assessment studies.
The repository is structured with an AVrateVoyager folder containing the modified software for collecting mouse movement data, and a dataset folder with the actual mouse movement recordings. This behavioral interaction data provides insights into how users navigate and interact with video content during quality assessment tasks, particularly in different testing environments.
The dataset is part of the broader research efforts at TU Ilmenau’s Audiovisual Technology Group on remote testing and crowdsourcing methodologies for video quality assessment. The research group has demonstrated that remote testing can be a reliable replacement for traditional laboratory tests, while acknowledging limitations such as lack of appropriate display devices and limitations of web technologies.
This dataset enables research on user behavior patterns during video quality assessment, comparison of user interaction across different testing paradigms (lab vs. crowd vs. out-of-lab), and development of improved quality assessment methodologies that account for user interaction patterns. The work contributes to understanding how crowdsourcing and remote testing approaches can complement traditional laboratory-based subjective quality assessment studies.
Access
Openly available for download from GitHub
License
Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)