Multimedia Communication Quality Assessment Testbeds
Edip Demirbilek, Jean-Charles Gr\'egoire

TL;DR
This paper compares multimedia quality assessment testbeds using VLC and GStreamer frameworks, demonstrating that GStreamer provides more robust streaming and more accurate quality metrics, leading to improved quality estimation models.
Contribution
The paper introduces a GStreamer-based multimedia pipeline that enhances robustness and accuracy in quality assessment, surpassing previous VLC-based methods.
Findings
GStreamer pipeline is more robust to network degradations.
RTCP statistics from GStreamer are more accurate.
Better quality estimation models were developed using improved data.
Abstract
We make an intensive use of multimedia frameworks in our research on modeling the perceived quality estimation in streaming services and real-time communications. In our preliminary work, we have used the VLC VOD software to generate reference audiovisual files with various degree of coding and network degradations. We have successfully built machine learning based models on the subjective quality dataset we have generated using these files. However, imperfections in the dataset introduced by the multimedia framework we have used prevented us from achieving the full potential of these models. In order to develop better models, we have re-created our end-to-end multimedia pipeline using the GStreamer framework for audio and video streaming. A GStreamer based pipeline proved to be significantly more robust to network degradations than the VLC VOD framework and allowed us to stream a…
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Taxonomy
TopicsMultimedia Communication and Technology · Image and Video Quality Assessment · Network Traffic and Congestion Control
