We present a new subjective and objective study on full high-definition (HD) stereoscopic (3D or S3D) video quality. In subjective study, we constructed an S3D video dataset with 12 pristine and 288 test videos, and the test videos are generated by applying the H.264 and H.265 compression, blur and frame freeze artifacts. We also propose a no reference (NR) objective video quality assessment (QA) algorithm that relies on measurements of the statistical dependencies between the motion and disparity subband coefficients of S3D videos. Inspired by the Generalized Gaussian Distribution (GGD) approach in liu2011statistical, we model the joint statistical dependencies between the motion and disparity components as following a Bivariate Generalized Gaussian Distribution (BGGD). We estimate the BGGD model parameters (α,β) and the coherence measure (Ψ) from the eigenvalues of the sample covariance matrix (M) of the BGGD. In turn, we model the BGGD parameters of pristine S3D videos using a Multivariate Gaussian (MVG) distribution. The likelihood of a test video's MVG model parameters coming from the pristine MVG model is computed and shown to play a key role in the overall quality estimation. We also estimate the global motion content of each video by averaging the SSIM scores between pairs of successive video frames. To estimate the test S3D video's spatial quality, we apply the popular 2D NR unsupervised NIQE image QA model on a frame-by-frame basis on both views. The overall quality of a test S3D video is finally computed by pooling the test S3D video's likelihood estimates, global motion strength and spatial quality scores. The proposed algorithm, which is 'completely blind' (requiring no reference videos or training on subjective scores) is called the Motion and Disparity based 3D video quality evaluator (MoDi3D). We show that MoDi3D delivers competitive performance over a wide variety of datasets including the IRCCYN dataset, the WaterlooIVC Phase I dataset, the LFOVIA dataset and our proposed LFOVIAS3DPh2 S3D video dataset.

A continuous evaluation of the end user's quality-of-experience (QoE) is essential for efficient video streaming. This is crucial for networks with constrained resources that offer time-varying channel quality to its users. In hypertext transfer protocol-based video streaming, the QoE is measured by quantifying the perceptual impact of distortions caused by rate adaptation or interruptions in playback due to rebuffering events. The resulting impact on the QoE due to these distortions has been studied individually in the literature. However, the QoE is determined by an interplay of these distortions, and therefore necessitates a combined study of them. To the best of our knowledge, there is no publicly available database that studies these distortions jointly on a continuous time basis. In this paper, our contributions are twofold. First, we present a database consisting of videos at full high definition and ultrahigh definition resolutions. We consider various levels of rate adaptation and rebuffering distortions together in these videos as experienced in a typical realistic setting. A subjective evaluation of these videos is conducted on a continuous time scale. Second, we present a QoE evaluation framework comprising a learning-based model during playback and an exponential model during rebuffering. Furthermore, we perform an objective evaluation of popular video quality assessment and continuous time QoE metrics over the constructed database. The objective evaluation study demonstrates that the performance of the proposed QoE model is superior to that of the objective metrics. The database is publicly available for download at http://www.iith.ac.in/~lfovia/downloads.html.

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.

We present an optical flow-based no-reference video quality assessment (NR-VQA) algorithm for assessing the perceptual quality of natural videos. Our algorithm is based on the hypothesis that distortions affect flow statistics both locally and globally. To capture the effects of distortion on optical flow, we measure irregularities at the patch level and at the frame level. At the patch level, we measure intra- and inter-patch level irregularities in the flow magnitude's variance and mean. We also measure the correlation in the patch level flow randomness between successive frames. At the frame level, we measure the normalized mean flow magnitude difference between successive frames. We rely on the robust NIQE algorithm for no-reference spatial quality assessment of the frames. These temporal and spatial features are averaged over all the frames to arrive at a video level feature vector. The video level features and the corresponding DMOS scores are used to train a support vector machine for regression (SVR). This machine is used to estimate the quality score of a test video. The competence of the proposed method is clearly demonstrated on SD and HD video databases that include common distortion types such as compression artifacts, packet loss artifacts, additive noise, and blur.

We present a simple yet effective optical flow-based full-reference video quality assessment (FR-VQA) algorithm for assessing the perceptual quality of natural videos. Our algorithm is based on the premise that local optical flow statistics are affected by distortions and the deviation from pristine flow statistics is proportional to the amount of distortion. We characterize the local flow statistics using the mean, the standard deviation, the coefficient of variation (CV), and the minimum eigenvalue ( λ min ) of the local flow patches. Temporal distortion is estimated as the change in the CV of the distorted flow with respect to the reference flow, and the correlation between λ min of the reference and of the distorted patches. We rely on the robust multi-scale structural similarity index for spatial quality estimation. The computed temporal and spatial distortions, thus, are then pooled using a perceptually motivated heuristic to generate a spatio-temporal quality score. The proposed method is shown to be competitive with the state-of-the-art when evaluated on the LIVE SD database, the EPFL Polimi SD database, and the LIVE Mobile HD database. The distortions considered in these databases include those due to compression, packet-loss, wireless channel errors, and rate-adaptation. Our algorithm is flexible enough to allow for any robust FR spatial distortion metric for spatial distortion estimation. In addition, the proposed method is not only parameter-free but also independent of the choice of the optical flow algorithm. Finally, we show that the replacement of the optical flow vectors in our proposed method with the much coarser block motion vectors also results in an acceptable FR-VQA algorithm. Our algorithm is called the flow similarity index.

Sparsity-based Distance Measure (SDM), a sparse reconstruction-based image similarity measure was recently proposed and shown to have promising applications in image classification, clustering and retrieval. In this paper, we present a statistical evaluation of SDM's performance as an image quality assessment (IQA) algorithm. This evaluation is carried out on the LIVE image database. We show that the SDM performs fairly in comparison with the state-of-the-art while possessing several attractive properties. Specifically, we demonstrate its robustness to rotation (90°, 180°), scaling, and combinations of distortions - properties that are highly desirable of any IQA algorithm.