No-reference Stereoscopic Image Quality Assessment Using Natural Scene Statistics

We present two contributions in this work: (i) a bivariate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity subband coefficients of natural stereoscopic scenes and (ii) a no-reference (NR) stereo image quality assessment algorithm based on the BGGD model. We first empirically show that a BGGD accurately models the joint distribution of luminance and disparity subband coefficients. We then show that the model parameters form good discriminatory features for NR quality assessment. Additionally, we rely on the previously established result that luminance and disparity subband coefficients of natural stereo scenes are correlated, and show that correlation also forms a good feature for NR quality assessment. These features are computed for both the left and right luminance-disparity pairs in the stereo image and consolidated into one feature vector per stereo pair. This feature set and the stereo pair׳s difference mean opinion score (DMOS) (labels) are used for supervised learning with a support vector machine (SVM). Support vector regression is used to estimate the perceptual quality of a test stereo image pair. The performance of the algorithm is evaluated over popular databases and shown to be competitive with the state-of-the-art no-reference quality assessment algorithms. Further, the strength of the proposed algorithm is demonstrated by its consistently good performance over both symmetric and asymmetric distortion types. Our algorithm is called Stereo QUality Evaluator (StereoQUE)

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

Virtual viewpoint three-dimensional panorama

Conventional panoramic images are known to provide for an enhanced field of view in which the scene always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual viewpoint creation to generate different panoramic images of the same scene with three-dimensional component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of panoramic images