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  • Cognitive no-reference video quality assessment for mobile streaming services

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    Tjondronegoro224581-Accepted.pdf (328.9Kb)
    Author(s)
    Vega, MT
    Giordano, E
    Mocanu, DC
    Tjondronegoro, D
    Liotta, A
    Griffith University Author(s)
    Tjondronegoro, Dian W.
    Year published
    2015
    Metadata
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    Abstract
    The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational ...
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    The evaluation of mobile streaming services, particularly in terms of delivered Quality of Experience (QoE), entails the use of automated methods (which excludes subjective QoE) that can be executed in real-time (i.e. without delaying the streaming process). This calls for lightweight algorithms that provide accurate results under considerable constraints. Starting from a low complexity no-reference objective algorithm for still images, in this work we contribute a new version that not only works for videos but, is general enough to adjust to a diverse range of video types while not significantly increasing the computational complexity. To achieve the necessary level of flexibility and computational efficiency, our method relies merely on information available at the client side and is equipped with a lightweight Artificial Neural Network which makes the algorithm independent from type of network or video. Its resource efficiency and generality make our method fit to be used in mobile streaming services. To prove the viability of our approach, we show a high level of correlation with the well-known full-reference method SSIM.
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    Conference Title
    2015 7th International Workshop on Quality of Multimedia Experience (QoMEX 2015)
    DOI
    https://doi.org/10.1109/QoMEX.2015.7148128
    Copyright Statement
    © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial Intelligence and Image Processing
    Publication URI
    http://hdl.handle.net/10072/390265
    Collection
    • Conference outputs

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