Methods for reducing visual discomfort in stereoscopic 3D: A review

This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”

Visual attention models and applications to 3D computer graphics

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Ph. D.) -- Bilkent University, 2012.Includes bibliographical refences.3D computer graphics, with the increasing technological and computational opportunities, have advanced to very high levels that it is possible to generate very realistic computer-generated scenes in real-time for games and other interactive environments. However, we cannot claim that computer graphics research has reached to its limits. Rendering photo-realistic scenes still cannot be achieved in real-time; and improving visual quality and decreasing computational costs are still research areas of great interest. Recent e orts in computer graphics have been directed towards exploiting principles of human visual perception to increase visual quality of rendering. This is natural since in computer graphics, the main source of evaluation is the judgment of people, which is based on their perception. In this thesis, our aim is to extend the use of perceptual principles in computer graphics. Our contribution is two-fold: First, we present several models to determine the visually important, salient, regions in a 3D scene. Secondly, we contribute to use of de nition of saliency metrics in computer graphics. Human visual attention is composed of two components, the rst component is the stimuli-oriented, bottom-up, visual attention; and the second component is task-oriented, top-down visual attention. The main di erence between these components is the role of the user. In the top-down component, viewer's intention and task a ect perception of the visual scene as opposed to the bottom-up component. We mostly investigate the bottom-up component where saliency resides. We de ne saliency computation metrics for two types of graphical contents. Our rst metric is applicable to 3D mesh models that are possibly animating, and it extracts saliency values for each vertex of the mesh models. The second metric we propose is applicable to animating objects and nds visually important objects due to their motion behaviours. In a third model, we present how to adapt the second metric for the animated 3D meshes. Along with the metrics of saliency, we also present possible application areas and a perceptual method to accelerate stereoscopic rendering, which is based on binocular vision principles and makes use of saliency information in a stereoscopic rendering scene. Each of the proposed models are evaluated with formal experiments. The proposed saliency metrics are evaluated via eye-tracker based experiments and the computationally salient regions are found to attract more attention in practice too. For the stereoscopic optimization part, we have performed a detailed experiment and veri ed our model of optimization. In conclusion, this thesis extends the use of human visual system principles in 3D computer graphics, especially in terms of saliency.Bülbül, Muhammed AbdullahPh.D

Saillance Visuelle, de la 2D à la 3D Stéréoscopique : Examen des Méthodes Psychophysique et Modélisation Computationnelle

Visual attention is one of the most important mechanisms deployed in the human visual system to reduce the amount of information that our brain needs to process. An increasing amount of efforts are being dedicated in the studies of visual attention, particularly in computational modeling of visual attention. In this thesis, we present studies focusing on several aspects of the research of visual attention. Our works can be mainly classified into two parts. The first part concerns ground truths used in the studies related to visual attention ; the second part contains studies related to the modeling of visual attention for Stereoscopic 3D (S-3D) viewing condition. In the first part, our work starts with identifying the reliability of FDM from different eye-tracking databases. Then we quantitatively identify the similarities and difference between fixation density maps and visual importance map, which have been two widely used ground truth for attention-related applications. Next, to solve the problem of lacking ground truth in the community of 3D visual attention modeling, we conduct a binocular eye-tracking experiment to create a new eye-tracking database for S-3D images. In the second part, we start with examining the impact of depth on visual attention in S-3D viewing condition. We firstly introduce a so-called "depth-bias" in the viewing of synthetic S-3D content on planar stereoscopic display. Then, we extend our study from synthetic stimuli to natural content S-3D images. We propose a depth-saliency-based model of 3D visual attention, which relies on depth contrast of the scene. Two different ways of applying depth information in S-3D visual attention model are also compared in our study. Next, we study the difference of center-bias between 2D and S-3D viewing conditions, and further integrate the center-bias with S-3D visual attention modeling. At the end, based on the assumption that visual attention can be used for improving Quality of Experience of 3D-TV when collaborating with blur, we study the influence of blur on depth perception and blur's relationship with binocular disparity.L'attention visuelle est l'un des mécanismes les plus importants mis en oeuvre par le système visuel humain (SVH) afin de réduire la quantité d'information que le cerveau a besoin de traiter pour appréhender le contenu d'une scène. Un nombre croissant de travaux est consacré à l'étude de l'attention visuelle, et en particulier à sa modélisation computationnelle. Dans cette thèse, nous présentons des études portant sur plusieurs aspects de cette recherche. Nos travaux peuvent être classés globalement en deux parties. La première concerne les questions liées à la vérité de terrain utilisée, la seconde est relative à la modélisation de l'attention visuelle dans des conditions de visualisation 3D. Dans la première partie, nous analysons la fiabilité de cartes de densité de fixation issues de différentes bases de données occulométriques. Ensuite, nous identifions quantitativement les similitudes et les différences entre carte de densité de fixation et carte d'importance visuelle, ces deux types de carte étant les vérités de terrain communément utilisées par les applications relatives à l'attention. Puis, pour faire face au manque de vérité de terrain exploitable pour la modélisation de l'attention visuelle 3D, nous procédons à une expérimentation oculométrique binoculaire qui aboutit à la création d'une nouvelle base de données avec des images stéréoscopiques 3D. Dans la seconde partie, nous commençons par examiner l'impact de la profondeur sur l'attention visuelle dans des conditions de visualisation 3D. Nous quantifions d'abord le " biais de profondeur " lié à la visualisation de contenus synthétiques 3D sur écran plat stéréoscopique. Ensuite, nous étendons notre étude avec l'usage d'images 3D au contenu naturel. Nous proposons un modèle de l'attention visuelle 3D basé saillance de profondeur, modèle qui repose sur le contraste de profondeur de la scène. Deux façons différentes d'exploiter l'information de profondeur par notre modèle sont comparées. Ensuite, nous étudions le biais central et les différences qui existent selon que les conditions de visualisation soient 2D ou 3D. Nous intégrons aussi le biais central à notre modèle de l'attention visuelle 3D. Enfin, considérant que l'attention visuelle combinée à une technique de floutage peut améliorer la qualité d'expérience de la TV-3D, nous étudions l'influence de flou sur la perception de la profondeur, et la relation du flou avec la disparité binoculaire

Stereoscopic image stitching with rectangular boundaries

This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience

Comfort-driven disparity adjustment for stereoscopic video

Pixel disparity—the offset of corresponding pixels between left and right views—is a crucial parameter in stereoscopic three-dimensional (S3D) video, as it determines the depth perceived by the human visual system (HVS). Unsuitable pixel disparity distribution throughout an S3D video may lead to visual discomfort. We present a unified and extensible stereoscopic video disparity adjustment framework which improves the viewing experience for an S3D video by keeping the perceived 3D appearance as unchanged as possible while minimizing discomfort. We first analyse disparity and motion attributes of S3D video in general, then derive a wide-ranging visual discomfort metric from existing perceptual comfort models. An objective function based on this metric is used as the basis of a hierarchical optimisation method to find a disparity mapping function for each input video frame. Warping-based disparity manipulation is then applied to the input video to generate the output video, using the desired disparity mappings as constraints. Our comfort metric takes into account disparity range, motion, and stereoscopic window violation; the framework could easily be extended to use further visual comfort models. We demonstrate the power of our approach using both animated cartoons and real S3D videos