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IEEE 3333.1.3-2022

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IEEE Standard for the Deep Learning-Based Assessment of Visual Experience Based on Human Factors

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New IEEE Standard - Active. Measuring quality of experience (QoE) aims to explore the factors that contribute to a user's perceptual experience including human, system, and context factors. Since QoE stems from human interaction with various devices, the estimation should be started by investigating the mechanism of human visual perception. Therefore, measuring QoE is still a challenging task. In this standard, QoE assessment is categorized into two subcategories which are perceptual quality and virtual reality (VR) cybersickness. In addition, deep learning models considering human factors for various QoE assessments are covered, along with a reliable subjective test methodology and a database construction procedure.
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

SDO IEEE: Institute of Electrical and Electronics Engineers
Document Number 3333.1.3
Publication Date May 27, 2022
Language en - English
Page Count 51
Revision Level
Supercedes
Committee Standards Activities Board Standards Committee
Publish Date Document Id Type View
May 27, 2022 3333.1.3-2022 Revision