Chi-Jui Chang’s research while affiliated with Academia Sinica and other places

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Publications (3)


Radar Chart: Scanning for Satisfactory QoE in QoS Dimensions
  • Article

July 2012

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47 Reads

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21 Citations

IEEE Network

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Chi-Jui Chang

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The ongoing convergence of QoE and QoS studies to give a thorough understanding of the end user has posed numerous exciting possibilities for network and multimedia researchers. However, there is not yet a proper visualization tool that is able to map the many-to-one relationship between QoS metrics and QoE, leaving researchers speechless in the cacophony of traditional two-dimensional diagrams. Although mostly employed in qualitative analysis, we found the radar chart, with a few tweaks, surprisingly suitable for the purpose. In this article, we present our adaptation of the radar chart, and demonstrate in a voice-over-IP context its use in single- and cross-application performance analysis, application recommendation, and network diagnosis.


Radar chart: Scanning for high QoE in QoS dimensions
  • Conference Paper
  • Full-text available

July 2010

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245 Reads

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10 Citations

The ongoing convergence of QoE (Quality of Experience) and QoS (Quality of Service) studies to give a thorough understanding of the end-user has posed numerous exciting possibilities for network and multimedia researchers. However, there is not yet a proper visualization tool that is able to map the many-to-one relationship between QoS metrics and QoE, leaving researchers speechless in the cacophony of traditional two-dimensional diagrams. Though mostly employed in qualitative analysis, we found that the radar chart, with a few tweaks, surprisingly suitable for the purpose. In this article, we present our adaptation of the radar chart, and demonstrate in a Voice-over-IP context its use in single- and cross-application performance analysis, application recommendation, and network diagnosis.

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Fig. 2: Flowchart of a researcher conducting an experiment.  
Fig. 5: QoE scores of VoIP recordings encoded by two codecs at various packet loss rates. the POSIX timestamp of the result down to milliseconds, and vcode the verification code unique to the result. Each line within the log represents a judgment between a comparable pair with the format stimulus_A stimulus_B (A|B) time,  
Fig. 6: QoE scores of a video clip repaired with two loss concealment schemes at various packet loss rates.
Quadrant of euphoria: A crowdsourcing platform for QoE assessment

May 2010

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516 Reads

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117 Citations

IEEE Network

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Chi-Jui Chang

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Existing quality of experience assessment methods, subjective or objective, suffer from either or both problems of inaccurate experiment tools and expensive personnel cost. The panacea for them, as we have come to realize, lies in the joint application of paired comparison and crowdsourcing, the latter being a Web 2.0 practice of organizations asking ordinary unspecific Internet users to carry out internal tasks. We present in this article Quadrant of Euphoria, a user-friendly Web-based platform facilitating QoE assessments in network and multimedia studies, which features low cost, participant diversity, meaningful and interpretable QoE scores, subject consistency assurance, and a burdenless experiment process.

Citations (3)


... Each attribute represents a specific characteristic or feature of the product, such as quality, performance, features, price, or customer support. The radar simultaneously hart provides a holistic view of how the benchmark products permission by plotting the data points for each attribute [32,33]. ...

Reference:

AI-Driven Transformation: Revolutionizing Production Management with Machine Learning and Data Visualization
Radar Chart: Scanning for Satisfactory QoE in QoS Dimensions
  • Citing Article
  • July 2012

IEEE Network

... Observing the patterns and shapes formed by the data points makes it easier to identify areas where a product excels or lags. This information can be invaluable for decisionmaking and prioritizing improvements or investments in specific areas [36,37]. ...

Radar chart: Scanning for high QoE in QoS dimensions

... As demonstrated in Tab. 1, public VQA datasets are significantly smaller in size when compared to video classification datasets [25,26]. The primary reason for this discrepancy is the prevalent use of crowdsourcing to annotate subjective video quality [5,9,22,44], which is a time-consuming yet indispensable process in eliminating randomness and enhancing consistency. For example, the KoNViD-1k dataset [23] requires an average of 114 subjective scores to produce a valid label. ...

Quadrant of euphoria: A crowdsourcing platform for QoE assessment

IEEE Network