This paper presents a system and methodology for the analysis of Quality of Experience (QoE) factors for dense multiview (MV) video using a Head-Mounted Device (HMD). An MV-HMD player has been designed and implemented to immerse the users in a virtual environment, where they are placed in front of a virtual lightfield display that shows a different viewpoint depending on the position of their head. The paper describes a methodology for the analysis of the subjective perception of the transition among views (motion parallax), which is specific to the visualization of multiview content. While previous works simulated the user movement by predefined view paths or used complex devices to track them, this system allows the observer to move freely, varying the perspective of the scene, while easily tracking the observer’s position. This work is, up to our knowledge, the first providing a complete framework for the assessment of this subjective factor using an HMD. The subjective results obtained using this framework are used to i) assess the influence of the user movement, display settings, and content characteristics in the perception of smoothness in the view transition, and ii) analyze the performance and limitations of a prediction model for subjective smoothness scores.
This paper presents a system and methodology for the analysis of Quality of Experience (QoE) factors for dense multiview (MV) video using a Head-Mounted Device (HMD). An MV-HMD player has been designed and implemented to immerse the users in a virtual environment, where they are placed in front of a virtual lightfield display that shows a different viewpoint depending on the position of their head. The paper describes a methodology for the analysis of the subjective perception of the transition among views (motion parallax), which is specific to the visualization of multiview content. While previous works simulated the user movement by predefined view paths or used complex devices to track them, this system allows the observer to move freely, varying the perspective of the scene, while easily tracking the observer’s position. This work is, up to our knowledge, the first providing a complete framework for the assessment of this subjective factor using an HMD. The subjective results obtained using this framework are used to i) assess the influence of the user movement, display settings, and content characteristics in the perception of smoothness in the view transition, and ii) analyze the performance and limitations of a prediction model for subjective smoothness scores. Read More
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