153 research outputs found

    GazeStereo3D: seamless disparity manipulations

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    Producing a high quality stereoscopic impression on current displays is a challenging task. The content has to be carefully prepared in order to maintain visual comfort, which typically affects the quality of depth reproduction. In this work, we show that this problem can be significantly alleviated when the eye fixation regions can be roughly estimated. We propose a new method for stereoscopic depth adjustment that utilizes eye tracking or other gaze prediction information. The key idea that distinguishes our approach from the previous work is to apply gradual depth adjustments at the eye fixation stage, so that they remain unnoticeable. To this end, we measure the limits imposed on the speed of disparity changes in various depth adjustment scenarios, and formulate a new model that can guide such seamless stereoscopic content processing. Based on this model, we propose a real-time controller that applies local manipulations to stereoscopic content to find the optimum between depth reproduction and visual comfort. We show that the controller is mostly immune to the limitations of low-cost eye tracking solutions. We also demonstrate benefits of our model in off-line applications, such as stereoscopic movie production, where skillful directors can reliably guide and predict viewers' attention or where attended image regions are identified during eye tracking sessions. We validate both our model and the controller in a series of user experiments. They show significant improvements in depth perception without sacrificing the visual quality when our techniques are applied

    Comfort-driven disparity adjustment for stereoscopic video

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    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

    Attention-Aware Disparity Control in interactive environments

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    Cataloged from PDF version of article.Our paper introduces a novel approach for controlling stereo camera parameters in interactive 3D environments in a way that specifically addresses the interplay of binocular depth perception and saliency of scene contents. Our proposed Dynamic Attention-Aware Disparity Control (DADC) method produces depth-rich stereo rendering that improves viewer comfort through joint optimization of stereo parameters. While constructing the optimization model, we consider the importance of scene elements, as well as their distance to the camera and the locus of attention on the display. Our method also optimizes the depth effect of a given scene by considering the individual user’s stereoscopic disparity range and comfortable viewing experience by controlling accommodation/convergence conflict. We validate our method in a formal user study that also reveals the advantages, such as superior quality and practical relevance, of considering our method.© Springer-Verlag Berlin Heidelberg 2013
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