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We present a no reference (NR) quality assessment algorithm for assessing theperceptual quality of natural stereoscopic 3D (S3D) videos. This work isinspired by our finding that the joint statistics of the subband coefficientsof motion (optical flow or motion vector magnitude) and depth (disparity map)of natural S3D videos possess a unique signature. Specifically, we empiricallyshow that the joint statistics of the motion and depth subband coefficients ofS3D video frames can be modeled accurately using a Bivariate GeneralizedGaussian Distribution (BGGD). We then demonstrate that the parameters of theBGGD model possess the ability to discern quality variations in S3D videos.Therefore, the BGGD model parameters are employed as motion and depth qualityfeatures. In addition to these features, we rely on a frame level spatialquality feature that is computed using a robust off the shelf NR image qualityassessment (IQA) algorithm. These frame level motion, depth and spatialfeatures are consolidated and used with the corresponding S3D video'sdifference mean opinion score (DMOS) labels for supervised learning usingsupport vector regression (SVR). The overall quality of an S3D video iscomputed by averaging the frame level quality predictions of the constituentvideo frames. The proposed algorithm, dubbed Video QUality Evaluation usingMOtion and DEpth Statistics (VQUEMODES) is shown to outperform the state of theart methods when evaluated over the IRCCYN and LFOVIA S3D subjective qualityassessment databases.

No Reference Stereoscopic Video Quality Assessment Using Joint Motion and Depth Statistics