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)

Quality index for stereoscopic images by jointly evaluating cyclopean amplitude and cyclopean phase

With widespread applications of three-dimensional (3-D) technology, measuring quality of experience for 3-D multimedia content plays an increasingly important role. In this paper, we propose a full reference stereo image quality assessment (SIQA) framework which focuses on the innovation of binocular visual properties and applications of low-level features. On one hand, based on the fact that human visual system understands an image mainly according to its low-level features, local phase and local amplitude extracted from phase congruency measurement are employed as primary features. Considering the less prominent performance of amplitude in IQA, visual saliency is applied into the modification on amplitude. On the other hand, by fully considering binocular rivalry phenomena, we create the cyclopean amplitude map and cyclopean phase map. With this method, both image features and binocular visual properties are mutually combined with each other. Meanwhile, a novel binocular modulation function in spatial domain is also adopted into the overall quality prediction of amplitude and phase. Extensive experiments demonstrate that the proposed framework achieves higher consistency with subjective tests than relevant SIQA metrics

Motion Parallax in Stereo 3D: Model and Applications

Binocular disparity is the main depth cue that makes stereoscopic images appear 3D. However, in many scenarios, the range of depth that can be reproduced by this cue is greatly limited and typically fixed due to constraints imposed by displays. For example, due to the low angular resolution of current automultiscopic screens, they can only reproduce a shallow depth range. In this work, we study the motion parallax cue, which is a relatively strong depth cue, and can be freely reproduced even on a 2D screen without any limits. We exploit the fact that in many practical scenarios, motion parallax provides sufficiently strong depth information that the presence of binocular depth cues can be reduced through aggressive disparity compression. To assess the strength of the effect we conduct psycho-visual experiments that measure the influence of motion parallax on depth perception and relate it to the depth resulting from binocular disparity. Based on the measurements, we propose a joint disparity-parallax computational model that predicts apparent depth resulting from both cues. We demonstrate how this model can be applied in the context of stereo and multiscopic image processing, and propose new disparity manipulation techniques, which first quantify depth obtained from motion parallax, and then adjust binocular disparity information accordingly. This allows us to manipulate the disparity signal according to the strength of motion parallax to improve the overall depth reproduction. This technique is validated in additional experiments

Full-reference stereoscopic video quality assessment using a motion sensitive HVS model

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics

Methods for reducing visual discomfort in stereoscopic 3D: A review

This work was supported by the EPSRC Grant EP/M01469X/1, “Geometric Evaluation of Stereoscopic Video”

Metrics for Stereoscopic Image Compression

Metrics for automatically predicting the compression settings for stereoscopic images, to minimize file size, while still maintaining an acceptable level of image quality are investigated. This research evaluates whether symmetric or asymmetric compression produces a better quality of stereoscopic image. Initially, how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly compressed stereoscopic image pairs was investigated. Two trials with human subjects, following the ITU-R BT.500-11 Double Stimulus Continuous Quality Scale (DSCQS) were undertaken to measure the quality of symmetric and asymmetric stereoscopic image compression. Computational models of the Human Visual System (HVS) were then investigated and a new stereoscopic image quality metric designed and implemented. The metric point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes in these regions. The PSNR results show that symmetric, as opposed to asymmetric stereo image compression, produces significantly better results. The human factors trial suggested that in general, symmetric compression of stereoscopic images should be used. The new metric, Stereo Band Limited Contrast, has been demonstrated as a better predictor of human image quality preference than PSNR and can be used to predict a perceptual threshold level for stereoscopic image compression. The threshold is the maximum compression that can be applied without the perceived image quality being altered. Overall, it is concluded that, symmetric, as opposed to asymmetric stereo image encoding, should be used for stereoscopic image compression. As PSNR measures of image quality are correctly criticized for correlating poorly with perceived visual quality, the new HVS based metric was developed. This metric produces a useful threshold to provide a practical starting point to decide the level of compression to use

The Effect of Applying 2D Enhancement Algorithms on 3D Video Content

abstract: Enhancement algorithms are typically applied to video content to increase their appeal to viewers. Such algorithms are readily available in the literature and are already widely applied in, for example, commercially available TVs. On the contrary, not much research has been done on enhancing stereoscopic 3D video content. In this paper, we present research focused on the effect of applying enhancement algorithms used for 2D content on 3D side-by-side content. We evaluate both offline enhancement of video content based on proprietary enhancement algorithms and real-time enhancement in the TVs. This is done using stereoscopic TVs with active shutter glasses, viewed both in their 2D and 3D viewing mode. The results of this research show that 2D enhancement algorithms are a viable first approach to enhance 3D content. In addition to video quality degradation due to the loss of spatial resolution as a consequence of the 3D video format, brightness reduction inherent to polarized or shutter glasses similarly degrades video quality. We illustrate the benefit of providing brightness enhancement for stereoscopic displays.View the article as published at https://www.hindawi.com/journals/jece/2014/601392