Predicting Visual Discomfort of Stereoscopic Images Using Human Attention Model

Jung, Yong Ju; Sohn, Hosik; Lee, Seong-il; Park, Hyun Wook; Ro, Yong Man

doi:10.1109/tcsvt.2013.2270394

Cited by 78 publications

(46 citation statements)

References 20 publications

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“…23 This database shows the results of a subjective evaluation following the guidelines given in the recommendations by the International Telecommunication Union. 49 KaistDB encompasses VD assessments based on a five-point grading scale of 120 images provided by 20 subjects in terms of mean opinion scores (MOSs).…”

Section: Methodsmentioning

confidence: 99%

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Improving Visual Discomfort Prediction for Stereoscopic Images via Disparity-based Contrast

Zellinger¹,

Moser²

2015

jist

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Stereoscopic images and videos can lead to serious adverse effects on human visual perception. The phenomenon of visual discomfort depends on various influencing factors such as the arrangement of the display system, the image quality and the design of 3D effects. Real-time depth adaptations that reduce the extent of visual discomfort require computationally efficient prediction models. This article analyzes optimal combinations of image features of state-of-the-art models in terms of prediction accuracy and computational efficiency. In addition, a fast-to-compute disparity contrast feature based on Haralick contrast is introduced in this context. It turns out that the computational complexity can be reduced by restricting the number of features without loss of prediction accuracy. A Pareto-front analysis shows which features are more likely to be part of optimal combinations. It is interesting to observe that the introduced disparity contrast feature is part of combinations that are optimal in terms of both computational efficiency and accuracy. This means that state-of-the-art prediction models can be improved by means of the introduced disparity contrast feature. The analysis relies on statistical evaluations based on publicly available assessment data.

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Section: Methodsmentioning

confidence: 99%

“…23 The article is structured as follows. The second section recapitulates some basic facts about VD.…”

Section: Introductionmentioning

confidence: 99%

Improving Visual Discomfort Prediction for Stereoscopic Images via Disparity-based Contrast

Zellinger¹,

Moser²

2015

jist

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“…The results obtained are quite accurate as illustrated in Fig.4 (c). In order to compute the depth saliency map, a simple but effective solution is adopted, assuming that foreground objects generally attract more attentions than the background in a scene [10,11], which is a reasonable assumption according to the previous literature [12]. Thus, the disparity values close to the maximum value are assigned to the high depth saliency values and the disparity values close to the minimum value are assigned to the low depth saliency values, as shown in Fig.…”

Section: Procedures Of the Proposed Metricmentioning

confidence: 99%

Perceived crosstalk assessment on patterned retarder 3D display

et al. 2014

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CONTEXT: Nowadays, almost all stereoscopic displays suffer from crosstalk, which is one of the most dominant degradation factors of image quality and visual comfort for 3D display devices. To deal with such problems, it is worthy to quantify the amount of perceived crosstalk. OBJECTIVE: Crosstalk measurements are usually based on some certain test patterns, but scene content effects are ignored. To evaluate the perceived crosstalk level for various scenes, subjective test may bring a more correct evaluation. However, it is a time consuming approach and is unsuitable for realtime applications. Therefore, an objective metric that can reliably predict the perceived crosstalk is needed. A correct objective assessment of crosstalk for different scene contents would be beneficial to the development of crosstalk minimization and cancellation algorithms which could be used to bring a good quality of experience to viewers. METHOD: A patterned retarder 3D display is used to present 3D images in our experiment. By considering the mechanism of this kind of devices, an appropriate simulation of crosstalk is realized by image processing techniques to assign different values of crosstalk to each other between image pairs. It can be seen from the literature that the structures of scenes have a significant impact on the perceived crosstalk, so we first extract the differences of the structural information between original and distorted image pairs through Structural SIMilarity (SSIM) algorithm, which could directly evaluate the structural changes between two complex-structured signals. Then the structural changes of left view and right view are computed respectively and combined to an overall distortion map. Under 3D viewing condition, because of the added value of depth, the crosstalk of pop-out objects may be more perceptible. To model this effect, the depth map of a stereo pair is generated and the depth information is filtered by the distortion map. Moreover, human attention is one of important factors for crosstalk assessment due to the fact that when viewing 3D contents, perceptual salient regions are highly likely to be a major contributor to determining the quality of experience of 3D contents. To take this into account, perceptual significant regions are extracted, and a spatial pooling technique is used to combine structural distortion map, depth map and visual salience map together to predict the perceived crosstalk more precisely. To verify the performance of the proposed crosstalk assessment metric, subjective experiments are conducted with 24 participants viewing and rating 60 simuli (5 scenes * 4 crosstalk levels * 3 camera distances). After an outliers removal and statistical process, the correlation with subjective test is examined using Pearson and Spearman rank-order correlation coefficient. Furthermore, the proposed method is also compared with two traditional 2D metrics, PSNR and SSIM. The objective score is mapped to subjective scale using a nonlinear fitting function to directly evaluate the performance of ...

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“…Due to the accommodation-vergence (AV) conflict in stereoscopic three-dimensional (S3D) displays and the human's ability of binocular fusion, current S3D displays can provide a high viewing quality of experience within a limited depth range only [1][2][3][4] . In addition, the viewing quality of S3D content, which is targeted for a particular viewing environment, cannot be always maintained as the perceived depth range of the content varies with different display sizes and viewing distances 5,6 .…”

Section: Introductionmentioning

confidence: 99%

“…The simplest approach to enhance the depth quality of S3D content may be to scale the disparity range of the scene. However, it is not always applicable since the allowable depth range in S3D displays is very limited in most cases [1][2][3] . Therefore, given the limited depth range, enhancing the perceived relative depth is very effective to enhance the depth quality of the scene.…”

Section: Introductionmentioning

confidence: 99%

Local disparity remapping to enhance depth quality of stereoscopic 3D images using stereoacuity function

Sohn

Jung

2014

SPIE Proceedings

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This paper proposes a simple but effective method for local disparity remapping that is capable of enhancing the depth quality of stereoscopic 3D images. In order to identify and scale the unperceivable difference in disparities in a scene, the proposed approach decomposes the disparity map into a two disparity layers: one is the coarse disparity layer that contains the information of the global depth structures and the other is the detail disparity layer that contains the details of the depth structures. Then, the proposed method adaptively manipulates the detail disparity layer in depth and image spaces under the guidance of a stereoacuity function, which describes the minimum amount of perceivable disparity difference given a disparity magnitude. In this way, relative depths between objects (or regions) can be effectively emphasized while providing a spatial adaptability in the image space. Experimental results showed that the proposed method was capable of improving the depth quality and overall viewing quality of stereoscopic 3D images as well.

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Predicting Visual Discomfort of Stereoscopic Images Using Human Attention Model

Cited by 78 publications

References 20 publications

Improving Visual Discomfort Prediction for Stereoscopic Images via Disparity-based Contrast

Improving Visual Discomfort Prediction for Stereoscopic Images via Disparity-based Contrast

Perceived crosstalk assessment on patterned retarder 3D display

Local disparity remapping to enhance depth quality of stereoscopic 3D images using stereoacuity function

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