Thèse - QoE evaluation and prediction in light-field and volumetric imaging
Thématique : Computer science 3D modelling and Modelling tools ; Cognitive science Technology
Etablissement : Polytech Nantes
Description du poste :
True hyper-realistic imaging is intended to provide the quality and accuracy of visual content that matches the capabilities of human visual system. To achieve this goal, visual content needs to be represented in multi-dimensional space, going beyond the traditional 2D + colour + time representation. Such new visual dimensions required for hyper realism include light field for motion parallax (free-view-point), binocular stereo, and focus cues; spatial and temporal resolution for sampling close to the limits of the eye; and colour and high dynamic range for accurate reproduction of all colours and light levels experienced in the real-world. All these perceptual aspects need to be considered at all technological stages of the hyper-realistic image delivery chain, from capture to the perception and quality evaluation. In this context, the goal of this PhD thesis is to develop new cognitive model, tools and measures to assess quality of experience (QoE) in light-field and volumetric imaging.
It includes the design of ad hoc experimental methodologies (direct and indirect approach) to characterize observer's experience and related analysis tools.
The PhD fellowship is funded by the European Training Network Realvision, a Marie Sklodowska Curie action of the European Commision which aims at training a new generation of scientists, technologists, and entrepreneurs that will move Europe into a leading role in innovative hyper-realistic imaging technologies.
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