Perceptual Quality-of-Experience of Stereoscopic 3D Images and Videos

With the fast development of 3D acquisition, communication, processing and display technologies, automatic quality assessment of 3D images and videos has become ever important. Nevertheless, recent progress on 3D image quality assessment (IQA) and video quality assessment (VQA) remains limited. The purpose of this research is to investigate various aspects of human visual quality-of-experience (QoE) when viewing stereoscopic 3D images/videos and to develop objective quality assessment models that automatically predict visual QoE of 3D images/videos. Firstly, we create a new subjective 3D-IQA database that has two features that are lacking in the literature, i.e., the inclusion of both 2D and 3D images, and the inclusion of mixed distortion types. We observe strong distortion type dependent bias when using the direct average of 2D image quality to predict 3D image quality. We propose a binocular rivalry inspired multi-scale model to predict the quality of stereoscopic images and the results show that the proposed model eliminates the prediction bias, leading to significantly improved quality predictions. Second, we carry out two subjective studies on depth perception of stereoscopic 3D images. The first one follows a traditional framework where subjects are asked to rate depth quality directly on distorted stereopairs. The second one uses a novel approach, where the stimuli are synthesized independent of the background image content and the subjects are asked to identify depth changes and label the polarities of depth. Our analysis shows that the second approach is much more effective at singling out the contributions of stereo cues in depth perception. We initialize the notion of depth perception difficulty index (DPDI) and propose a novel computational model for DPDI prediction. The results show that the proposed model leads to highly promising DPDI prediction performance. Thirdly, we carry out subjective 3D-VQA experiments on two databases that contain various asymmetrically compressed stereoscopic 3D videos. We then compare different mixed-distortions asymmetric stereoscopic video coding schemes with symmetric coding methods and verify their potential coding gains. We propose a model to account for the prediction bias from using direct averaging of 2D video quality to predict 3D video quality. The results show that the proposed model leads to significantly improved quality predictions and can help us predict the coding gain of mixed-distortions asymmetric video compression. Fourthly, we investigate the problem of objective quality assessment of Multi-view-plus-depth (MVD) images, with a main focus on the pre- depth-image-based-rendering (pre-DIBR) case. We find that existing IQA methods are difficult to be employed as a guiding criterion in the optimization of MVD video coding and transmission systems when applied post-DIBR. We propose a novel pre-DIBR method based on information content weighting of both texture and depth images, which demonstrates competitive performance against state-of-the-art IQA models applied post-DIBR

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

Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing

Free-viewpoint video conferencing allows a participant to observe the remote 3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint image is commonly synthesized using two pairs of transmitted texture and depth maps from two neighboring captured viewpoints via depth-image-based rendering (DIBR). To maintain high quality of synthesized images, it is imperative to contain the adverse effects of network packet losses that may arise during texture and depth video transmission. Towards this end, we develop an integrated approach that exploits the representation redundancy inherent in the multiple streamed videos a voxel in the 3D scene visible to two captured views is sampled and coded twice in the two views. In particular, at the receiver we first develop an error concealment strategy that adaptively blends corresponding pixels in the two captured views during DIBR, so that pixels from the more reliable transmitted view are weighted more heavily. We then couple it with a sender-side optimization of reference picture selection (RPS) during real-time video coding, so that blocks containing samples of voxels that are visible in both views are more error-resiliently coded in one view only, given adaptive blending will erase errors in the other view. Further, synthesized view distortion sensitivities to texture versus depth errors are analyzed, so that relative importance of texture and depth code blocks can be computed for system-wide RPS optimization. Experimental results show that the proposed scheme can outperform the use of a traditional feedback channel by up to 0.82 dB on average at 8% packet loss rate, and by as much as 3 dB for particular frames