Vector quantization and signal compression
Vector quantization and signal compression
Measuring perceived quality of speech and video in multimedia conferencing applications
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
IEEE MultiMedia
The effectiveness of a QoE-based video output scheme for audio-video ip transmission
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Modeling packet-loss visibility in MPEG-2 video
IEEE Transactions on Multimedia
A study of real-time packet video quality using random neural networks
IEEE Transactions on Circuits and Systems for Video Technology
Quantifying video-QoE degradations of internet links
IEEE/ACM Transactions on Networking (TON)
Wireless Personal Communications: An International Journal
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We present a scalable, lightweight, no-reference framework to infer video QoE. Our framework revolves around a one time offline construction of a k-dimensional space, which we call the QoE space. The k-dimensions accommodate k parameters (network-dependent/independent) that potentially affect video quality. The k-dimensional space is partitioned to N representative zones, each with a QoE index. Instantaneous parameter values are matched with the indices to infer QoE. To validate our framework, we construct a 3-dimensional QoE space with bit-rate, loss, and delay as the principal components. We create 18 video samples with unique combinations of the 3 parameters. 77 human subjects rated these video samples on a scale of 1 to 5 to create the QoE space. In a second set of survey, our predicted MOS was compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions. An implementation of our framework on standard Linux PC shows we can compute 20 MOS calculations per second with 3 parameters and 18 partitions of the QoE space.