Redundancy of stereoscopic images: Experimental Evaluation

With the recent advancement in visualization devices over the last years, we are seeing a growing market for stereoscopic content. In order to convey 3D content by means of stereoscopic displays, one needs to transmit and display at least 2 points of view of the video content. This has profound implications on the resources required to transmit the content, as well as demands on the complexity of the visualization system. It is known that stereoscopic images are redundant, which may prove useful for compression and may have positive effect on the construction of the visualization device. In this paper we describe an experimental evaluation of data redundancy in color stereoscopic images. In the experiments with computer generated and real life and test stereo images, several observers visually tested the stereopsis threshold and accuracy of parallax measuring in anaglyphs and stereograms as functions of the blur degree of one of two stereo images and color saturation threshold in one of two stereo images for which full color 3D perception with no visible color degradations is maintained. The experiments support a theoretical estimate that one has to add, to data required to reproduce one of two stereoscopic images, only several percents of that amount of data in order to achieve stereoscopic perception

Selective rendering for efficient ray traced stereoscopic images

Depth-related visual effects are a key feature of many virtual environments. In stereo-based systems, the depth effect can be produced by delivering frames of disparate image pairs, while in monocular environments, the viewer has to extract this depth information from a single image by examining details such as perspective and shadows. This paper investigates via a number of psychophysical experiments, whether we can reduce computational effort and still achieve perceptually high-quality rendering for stereo imagery. We examined selectively rendering the image pairs by exploiting the fusing capability and depth perception underlying human stereo vision. In ray-tracing-based global illumination systems, a higher image resolution introduces more computation to the rendering process since many more rays need to be traced. We first investigated whether we could utilise the human binocular fusing ability and significantly reduce the resolution of one of the image pairs and yet retain a high perceptual quality under stereo viewing condition. Secondly, we evaluated subjects' performance on a specific visual task that required accurate depth perception. We found that subjects required far fewer rendered depth cues in the stereo viewing environment to perform the task well. Avoiding rendering these detailed cues saved significant computational time. In fact it was possible to achieve a better task performance in the stereo viewing condition at a combined rendering time for the image pairs less than that required for the single monocular image. The outcome of this study suggests that we can produce more efficient stereo images for depth-related visual tasks by selective rendering and exploiting inherent features of human stereo vision

Saliency detection for stereoscopic images

International audienceSaliency detection techniques have been widely used in various 2D multimedia processing applications. Currently, the emerging applications of stereoscopic display require new saliency detection models for stereoscopic images. Different from saliency detection for 2D images, depth features have to be taken into account in saliency detection for stereoscopic images. In this paper, we propose a new stereoscopic saliency detection framework based on the feature contrast of color, intensity, texture, and depth. Four types of features including color, luminance, texture, and depth are extracted from DC-T coefficients to represent the energy for image patches. A Gaussian model of the spatial distance between image patches is adopted for the consideration of local and global contrast calculation. A new fusion method is designed to combine the feature maps for computing the final saliency map for stereoscopic images. Experimental results on a recent eye tracking database show the superior performance of the proposed method over other existing ones in saliency estimation for 3D images

Evaluating methods for controlling depth perception in stereoscopic cinematography.

Existing stereoscopic imaging algorithms can create static stereoscopic images with perceived depth control function to ensure a compelling 3D viewing experience without visual discomfort. However, current algorithms do not normally support standard Cinematic Storytelling techniques. These techniques, such as object movement, camera motion, and zooming, can result in dynamic scene depth change within and between a series of frames (shots) in stereoscopic cinematography. In this study, we empirically evaluate the following three types of stereoscopic imaging approaches that aim to address this problem. (1) Real-Eye Configuration: set camera separation equal to the nominal human eye interpupillary distance. The perceived depth on the display is identical to the scene depth without any distortion. (2) Mapping Algorithm: map the scene depth to a predefined range on the display to avoid excessive perceived depth. A new method that dynamically adjusts the depth mapping from scene space to display space is presented in addition to an existing fixed depth mapping method. (3) Depth of Field Simulation: apply Depth of Field (DOF) blur effect to stereoscopic images. Only objects that are inside the DOF are viewed in full sharpness. Objects that are far away from the focus plane are blurred. We performed a human-based trial using the ITU-R BT.500-11 Recommendation to compare the depth quality of stereoscopic video sequences generated by the above-mentioned imaging methods. Our results indicate that viewers' practical 3D viewing volumes are different for individual stereoscopic displays and viewers can cope with much larger perceived depth range in viewing stereoscopic cinematography in comparison to static stereoscopic images. Our new dynamic depth mapping method does have an advantage over the fixed depth mapping method in controlling stereo depth perception. The DOF blur effect does not provide the expected improvement for perceived depth quality control in 3D cinematography. We anticipate the results will be of particular interest to 3D filmmaking and real time computer games

Stereoscopic video chroma key processing using NVIDIA CUDA

In this paper, I use the NVIDIA CUDA technology to perform the chroma key algorithm on stereoscopic images. NVIDIA CUDA allows to process parallel algorithms on GPU. Input data are stereoscopic images with the monochromatic background and the destination background image. Output data is the combination of inputs by using the chroma key. I compare the algorithm efficiency between the GPU and CPU execution

Visual Comfort Assessment for Stereoscopic Image Retargeting

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception

Morphing a Stereogram into Hologram

This paper develops a simple and fast method to reconstruct reality from stereoscopic images. We bring together ideas from robust optical flow techniques, morphing deformations and lightfield 3D rendering in order to create unsupervised multiview images of a scene. The reconstruction algorithm provides a good visualization of the virtual 3D imagery behind stereograms upon display on a headset-free Looking Glass 3D monitor. We discuss the possibility of applying the method for live 3D streaming optimized via an associated lookup table.Comment: PDF, 8 pages, 4 Fig