No-Reference Quality Assessment of Natural Stereopairs

Chen, Mingjun; Cormack, Lawrence K.; Bovik, Alan C.

doi:10.1109/tip.2013.2267393

Cited by 248 publications

(140 citation statements)

References 36 publications

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“…It can be concluded that Q3D-RBM performs comparably with FF QA metrics, while it is a RR 3D QA metric, independent of distortions type. The second experiment was done on the Phase II of the LIVE 3D Image Quality Database [12,24]. This contains 8 reference images with 45 distorted images for each reference one.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Deep learning for objective quality assessment of 3D images

Mocanu

Exarchakos

Liotta

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Improving the users' Quality of Experience (QoE) in modern 3D Multimedia Systems is a challenging proposition, mainly due to our limited knowledge of 3D image Quality Assessment algorithms. While subjective QoE methods would better reflect the nature of human perception, these are not suitable in real-time automation cases. In this paper we tackle this issue from a new angle, using deep learning to make predictions on the user's QoE rather than trying to measure it through deterministic algorithms. We benchmark our method, dubbed Quality of Experience for 3D images through Factored Third Order Restricted Boltzmann Machine (Q3D-RBM), with subjective QoE methods, to determine its accuracy for different types of 3D images. The outcome is a Reduced Reference QoE assessment process for automatic image assessment and has significant potential to be extended to work on 3D video assessment.

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Section: Experiments and Resultsmentioning

confidence: 99%

Deep learning for objective quality assessment of 3D images

Mocanu

Exarchakos

Liotta

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…Pixels associated with disparity errors are often dissimilar with features computed on the corresponding disparity shifted pixels in the other view. The authors of [39] defined a disparity uncertainty map to estimate the uncertainty produced by DSSIM and used it as a feature to improve the task of 3D no-reference distortion assessment. The uncertainty is defined as:…”

Section: Uncertainty Mapmentioning

confidence: 99%

“…An 11 × 11 Gaussian weighting matrix with a space constant 3.67 pixels was used to compute μ and σ as in [39]. The uncertainty reflects the degree of similarity between the corresponding pixels of a stereo pair.…”

Section: Uncertainty Mapmentioning

confidence: 99%

“…Towards filling these gaps, we begin by studying the performance differences of S3D discomfort prediction models using three nominal disparity algorithms having different levels of complexity. We then introduce two new sets of discomfort predictive features, the uncertainty map and natural scene statistics, which have previously found use in 3D image quality assessment models [20][21][22]. These features efficiently improve the performance of prediction models that use low complexity disparity calculation methods.…”

Section: Introductionmentioning

confidence: 99%

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3D visual discomfort prediction using low complexity disparity algorithms

Chen

Zhou

Sun

et al. 2016

J Image Video Proc.

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Algorithms that predict the degree of visual discomfort experienced when viewing stereoscopic 3D (S3D) images usually first execute some form of disparity calculation. Following that, features are extracted on these disparity maps to build discomfort prediction models. These features may include, for example, the maximum disparity, disparity range, disparity energy, and other measures of the disparity distribution. Hence, the accuracy of prediction largely depends on the accuracy of disparity calculation. Unfortunately, computing disparity maps is expensive and difficult and most leading assessment models are based on features drawn from the outputs of high complexity disparity calculation algorithms that deliver high quality disparity maps. There is no consensus on the type of stereo matching algorithm that should be used for this type of model. Towards filling this gap, we study the relative performances of discomfort prediction models that use disparity algorithms having different levels of complexity. We also propose a set of new discomfort predictive features with good performance even when using low complexity disparity algorithms.

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“…Our measure of spatial saliency strength is computed on a fused representation of the left and right frames obtained using the cyclopean image model described in [24]. 1) Luminance: Several studies have been conducted on visual attention as a function of the luminance distribution of a stereoscopic image [3]- [14].…”

Section: A Spatial Saliency Strengthmentioning

confidence: 99%

Saliency Prediction on Stereoscopic Videos

Kim

Lee

Bovik

2014

IEEE Trans. on Image Process.

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Abstract-We describe a new 3D saliency prediction model that accounts for diverse low-level luminance, chrominance, motion, and depth attributes of 3D videos as well as high-level classifications of scenes by type. The model also accounts for perceptual factors, such as the nonuniform resolution of the human eye, stereoscopic limits imposed by Panum's fusional area, and the predicted degree of (dis) comfort felt, when viewing the 3D video. The high-level analysis involves classification of each 3D video scene by type with regard to estimated camera motion and the motions of objects in the videos. Decisions regarding the relative saliency of objects or regions are supported by data obtained through a series of eye-tracking experiments. The algorithm developed from the model elements operates by finding and segmenting salient 3D space-time regions in a video, then calculating the saliency strength of each segment using measured attributes of motion, disparity, texture, and the predicted degree of visual discomfort experienced. The saliency energy of both segmented objects and frames are weighted using models of human foveation and Panum's fusional area yielding a single predictor of 3D saliency.

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No-Reference Quality Assessment of Natural Stereopairs

Cited by 248 publications

References 36 publications

Deep learning for objective quality assessment of 3D images

Deep learning for objective quality assessment of 3D images

3D visual discomfort prediction using low complexity disparity algorithms

Saliency Prediction on Stereoscopic Videos

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