Full-reference quality assessment of stereopairs accounting for rivalry

Chen, Mingjun; Su, Chih‐Ying; Kwon, Do-Kyoung; Cormack, Lawrence K.; Bovik, Alan C.

doi:10.1016/j.image.2013.05.006

Cited by 314 publications

(250 citation statements)

References 46 publications

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“…Binocular vision contains a large amount of information, so it is necessary to map the common information in an image which can reflect left image and right image. The appearance of the cyclopean map has solved this problem [19,20].…”

Section: Binocular Visionmentioning

confidence: 99%

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RETRACTED ARTICLE: Quality assessment for virtual reality technology based on real scene

Jiang

Yang

Jiang³

et al. 2016

Neural Comput & Applic

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Virtual reality technology is a new display technology, which provides users with real viewing experience. As known, most of the virtual reality display through stereoscopic images. However, image quality will be influenced by the collection, storage and transmission process . If the stereoscopic image quality in the virtual reality technology is seriously damaged, the user will feel uncomfortable, and this can even cause healthy problems.In this paper, we establish a set of accurate and effective evaluation for the virtual reality. In the preprocessing, we segment the original reference and distorted image into binocular regions and monocular regions. Then, the IW-SSIM(Information weighted-SSIM) or IW-PSNR(Information weighted-PSNR) values over the monocular regions are applied to obtain the IW-score.At the same time, the SW-SSIM(Stereo weighted-SSIM)or SW-PSNR(Stereo weighted-PSNR) can be used to caculate the SW-score. Finally, we pool the stereoscopic images score by combing the IW-score and SW-score. Experiments show that our method is very consistent with human subjective judgment standard in evaluation of virtual reality technology.

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Section: Binocular Visionmentioning

confidence: 99%

“…At present, there are a lot of researches on the objective evaluation of stereo image quality [17][18][19] . Some papers construct the models of human visual system (HVS), which can evaluate the quality of the stereoscopic image [43].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Quality assessment for virtual reality technology based on real scene

Jiang

Yang

Jiang³

et al. 2016

Neural Comput & Applic

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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%

“…Both layers are superimposed by a layer of hidden neurons, using three way multiplicative interactions. We tested our approach on two benchmark databases, which include both, 3D images and subjective QoE results [12,13].…”

Section: Introductionmentioning

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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“…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%

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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Full-reference quality assessment of stereopairs accounting for rivalry

Cited by 314 publications

References 46 publications

RETRACTED ARTICLE: Quality assessment for virtual reality technology based on real scene

RETRACTED ARTICLE: Quality assessment for virtual reality technology based on real scene

Deep learning for objective quality assessment of 3D images

3D visual discomfort prediction using low complexity disparity algorithms

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