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)

No- Reference Stereoscopic Image Quality Assessment

We present two contributions in this work 1)a bi variate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity sub band coefficients of natural Stereoscopic scenes. and 2) a no- reference (NR) stereo image quality assessment algorithm based on the BGGD model

A full reference stereoscopic video quality assessment metric

We propose a full reference stereo video quality assessment algorithm for assessing the perceptual quality of natural stereo videos. We exploit the separable representation of motion and binocular disparity in the visual cortex and develop a four stage algorithm to measure the quality of a stereoscopic video called FLOSIM3D. First, we compute the temporal features by utilizing an existing 2D VQA metric which measures the temporal annoyance based on patch level statistics such as mean, variance and minimum eigen value and pools them with a frame categorization based non-linear pooling strategy. Second, a structure based 2D Image Quality Assessment (IQA) metric is used to compute the spatial quality of the frames. Next, the loss in depth cues is measured using a structure based metric. Finally, the features for each of the stereo views are pooled to obtain the final stereo video quality score. We demonstrate the state-of-the-art performance of the proposed metric on IRCCYN dataset involving H.264, JP2K compression artifacts

Full-Reference Stereo Image Quality Assessment Using Natural Stereo Scene Statistics

Empirical studies of the joint statistics of luminance and disparity images (or wavelet coefficients) of natural stereoscopic scenes have resulted in two important findings: a) the marginal statistics are modelled well by the generalized Gaussian distribution (GGD) and b) there exists significant correlation between them. Inspired by these findings, we propose a full-reference image quality assessment algorithm dubbed STeReoscopic Image Quality Evaluator (STRIQE). We show that the parameters of the GGD fits of luminance wavelet coefficients along with correlation values form excellent features. Importantly, we demonstrate that the use of disparity information (via correlation) results in a consistent improvement in the performance of the algorithm. The performance of our algorithm is evaluated over popular datasets and shown to be competitive with the state-of-the-art full-reference algorithms. The efficacy of the algorithm is further highlighted by its near-linear relation with subjective scores, low root mean squared error (RMSE), and consistently good performance over both symmetric and asymmetric distortions

No-reference image quality assessment using statistics of sparse representations

We present a no-reference (NR) image quality assessment (IQA) algorithm that is inspired by the representation of visual scenes in the primary visual cortex of the human visual system. Specifically, we use the sparse coding model of the area V1 to construct an overcomplete dictionary for sparsely representing pristine (undistorted) natural images. First, we empirically demonstrate that the distribution of the sparse representation coefficients of natural images have sharp peaks and heavy tails, and can therefore be modeled using a Univariate Generalized Gaussian Distribution (UGGD). We then show that the UGGD model parameters form good features for distortion estimation and formulate our no-reference IQA algorithm based on this observation. Subsequently, we find UGGD model parameters that are representative of the class of pristine natural images. This is achieved using a training set of undistorted natural images. The perceptual quality of a test image is then defined to be the likelihood of its sparse coefficients being generated from the pristine UGGD model. We show that the proposed algorithm correlates well with subjective evaluation over several standard image databases. Further, the proposed method allows us to construct a distortion map that has several useful applications like distortion localization, adaptive rate allocation etc. Finally and importantly, the proposed NR-IQA algorithm does not make use of any distortion information or subjective scores during the training process

Super-Multiview Content with High Angular Resolution: 3D Quality Assessment on Horizontal-Parallax Lightfield Display

The advent of glasses-free 3D super-multiview (SMV) displays has opened up new avenues for experiencing 3D content. Accordingly, perceptual quality assessment of such content assumes significance. In this context, we present the results of subjective and objective quality assessment of static 3D SMV content, conducted with twenty subjects on a glasses-free horizontal-parallax 3D lightfield display. First, we create a multiview image dataset using three real objects, with high angular resolution. The display system generates 3D views from fixed subsets of consecutive images. Next, we extend the standard guidelines of subjective assessment to our evaluation environment. In the course, we make recommendations on acquiring SMV data as well as conducting subjective evaluation. Subsequently, we observe that the existing 2D full-reference (FR) quality metrics, applied to individual 3D views, are inadequate for 3D quality assessment. To fill the gap, we propose a 3D FR objective quality metric. For every 3D view, the proposed metric combines spatial information from each constituent image and angular information (depth cues) from consecutive images. Finally, we show that the proposed metric correlates significantly with subjective scores, outperforming existing 2D metrics. The efficacy of pooling spatial and angular information highlights the fact that angular information plays a crucial role in 3D perception