Standard

IEEE 3333.1.3

Published

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Abstract

This standard defines deep learning-based metrics of content analysis and quality of experience (QoE) assessment for visual contents, which is an extension of the standard for the QoE and visual-comfort assessments of three-dimensional (3D) contents based on psychophysical studies (IEEE Std 3333.1.1™) and the standard for the perceptual quality assessment of 3D and ultra-high definition (UHD) contents (IEEE Std 3333.1.2™). The scope covers the following: — Deep learning models for QoE assessment (multilayer perceptrons, convolutional neural networks, deep generative models) — Deep metrics of visual experience from high definition (HD), UHD, 3D, high dynamic range (HDR), virtual reality (VR) and mixed reality (MR) contents — Deep analysis of clinical (electroencephalogram (EEG), electrocardiogram (ECG), electrooculography (EOG), and so on) and psychophysical [subjective test and simulator sickness questionnaire (SSQ)] data for QoE assessment — Deep personalized preference assessment of visual contents — Building image and video databases for performance benchmarking purpose if necessary

Document information

  • Standard from IEEE_AC
  • Published:
  • Version: 0
  • Document type: IS
  • Additional information
    • IEEE Computer Society