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

Improved content aware scene retargeting for retinitis pigmentosa patients

<p>Abstract</p> <p>Background</p> <p>In this paper we present a novel scene retargeting technique to reduce the visual scene while maintaining the size of the key features. The algorithm is scalable to implementation onto portable devices, and thus, has potential for augmented reality systems to provide visual support for those with tunnel vision. We therefore test the efficacy of our algorithm on shrinking the visual scene into the remaining field of view for those patients.</p> <p>Methods</p> <p>Simple spatial compression of visual scenes makes objects appear further away. We have therefore developed an algorithm which removes low importance information, maintaining the size of the significant features. Previous approaches in this field have included <it>seam carving</it>, which removes low importance seams from the scene, and <it>shrinkability </it>which dynamically shrinks the scene according to a generated importance map. The former method causes significant artifacts and the latter is inefficient. In this work we have developed a new algorithm, combining the best aspects of both these two previous methods. In particular, our approach is to generate a <it>shrinkability </it>importance map using as seam based approach. We then use it to dynamically shrink the scene in similar fashion to the <it>shrinkability </it>method. Importantly, we have implemented it so that it can be used in real time without prior knowledge of future frames.</p> <p>Results</p> <p>We have evaluated and compared our algorithm to the <it>seam carving </it>and image <it>shrinkability </it>approaches from a content preservation perspective and a compression quality perspective. Also our technique has been evaluated and tested on a trial included 20 participants with simulated tunnel vision. Results show the robustness of our method at reducing scenes up to 50% with minimal distortion. We also demonstrate efficacy in its use for those with simulated tunnel vision of 22 degrees of field of view or less.</p> <p>Conclusions</p> <p>Our approach allows us to perform content aware video resizing in real time using only information from previous frames to avoid jitter. Also our method has a great benefit over the ordinary resizing method and even over other image retargeting methods. We show that the benefit derived from this algorithm is significant to patients with fields of view 20° or less.</p

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”

How to measure the relevance of a retargeting approach?

International audienceMost cell phones today can receive and display video content. Nonetheless, we are still significantly behind the point where premium made for mobile content is mainstream, largely available, and affordable. Significant issues must be overcome. The small screen size is one of them. Indeed, the direct transfer of conventional contents (not specifically shot for mobile devices) will provide a video in which the main characters or objects of interest may become indistinguishable from the rest of the scene. Therefore, it is required to retarget the content. Different solutions exist, either based on distortion of the image, on removal of redundant areas, or cropping. The most efficient ones are based on dynamic adaptation of the cropping window. They significantly improve the viewing experience by zooming in the regions of interest. Currently, there is no common agreement on how to compare different solutions. A retargeting metric is proposed in order to gauge its quality. Eye-tracking experiments, zooming effect through coverage ratio and temporal consistency are introduced and discussed