Assessment of Stereoscopic Crosstalk Perception

Xing, Liyuan; You, Junyong; Ebrahimi, Touradj; Perkis, Andrew

doi:10.1109/tmm.2011.2172402

Cited by 46 publications

(43 citation statements)

References 14 publications

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“…For comparison, the SSIM [14] and Xing's algorithm [8] are tested with the same data set. Since the SSIM is originally designated for the image quality assessment, we altered the distorted image input by the crosstalk-simulated image.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Only a few metrics which can be applied to general stereoscopic images were proposed to predict the crosstalk quality. Xing et al defined the 2D and the 3D attributes that cause the perceptual crosstalk [8]. By combining the SSIM map and the disparity map of the natural stereoscopic images, the metric predicts the perceptual crosstalk score.…”

Section: Introductionmentioning

confidence: 99%

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Quality assessment of perceptual crosstalk in autostereoscopic display

Kim

2014

2014 IEEE International Conference on Image Processing (ICIP)

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Crosstalk is one of the most annoying problems in an autostereoscopic display causing perceptual quality degradation and visual discomfort. To predict the perceived crosstalk when viewing an autostereoscopic display, it is necessary to consider the characteristics of human perception, displaying mechanism, viewing environment and so on. Therefor, we propose a novel metric for predicting the perceptual crosstalk that is based on human visual system (HVS); non-linear sensitivity of luminance and masking effects. The proposed model adopts the duplicated structure masking, yielding predictive power that is statistically superior to prior models that rely on 2D quality metric.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality assessment of perceptual crosstalk in autostereoscopic display

Kim

2014

2014 IEEE International Conference on Image Processing (ICIP)

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“…All of the three components are combined and pooled to predict the perceived crosstalk. The SSIM quality measure proposed by Wang et al [5] can describe the 2D crosstalk region to some extent [2]. In our study, the structural dissimilarity map is constructed between the original image and the distorted version with both the system introduced and the simulated.…”

Section: Procedures Of the Proposed Metricmentioning

confidence: 99%

“…In addition, when different scene contents are considered, the closer the 3D depth of the foreground to the nearest 3D depth of the scene is, the more visible the perceived crosstalk is [2] [3], and it is known that human subjects tend to pay more attention to perceptually significant regions (such as image saliency regions [4]). In this study, we focus on an objective assessment metric for the crosstalk with different crosstalk levels, camera baselines and scene contents.…”

Section: Introductionmentioning

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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“…In [10], the performances of several state-of-the-art 2D quality metrics were compared for quantification of the quality of stereo pairs formed from two synthesized views. In [11] the authors studied the perception of stereoscopic crosstalk and performed a set of subjective tests to obtain mean opinion scores (MOS) of stereoscopic videos. They attempted to predict the MOS by combination of a structural similarity index (SSIM) map and pre-filtered dense disparity map.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the importance of cyclopean view and binocular rivalry-related features for objective quality assessment of mobile 3D video

et al. 2014

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Abstract3D video is expected to provide an enhanced user experience by using the impression of depth to bring greater realism to the user. Quality assessment plays an important role in the design and optimization of 3D video processing systems. In this paper, a new 3D image quality model that is specifically tailored for mobile 3D video is proposed. The model adopts three quality components, called the cyclopean view, binocular rivalry, and the scene geometry, in which the quality must be quantified. The cyclopean view formation process is simulated and its quality is evaluated using the three proposed approaches. Binocular rivalry is quantified over the distorted stereo pairs, and the scene quality is quantified over the disparity map. Based on the model, the 3D image quality can then be assessed using state-of-the-art 2D quality measures selected appropriately through a machine learning approach. To make the metric simple, fast, and efficient, final selection of the quality features is accomplished by also considering the computational complexity and the CPU running time. The metric is compared with several currently available 2D and 3D metrics. Experimental results show that the compound metric gives a significantly high correlation with the mean opinion scores that were collected through large-scale subjective tests run on mobile 3D video content.

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Assessment of Stereoscopic Crosstalk Perception

Cited by 46 publications

References 14 publications

Quality assessment of perceptual crosstalk in autostereoscopic display

Quality assessment of perceptual crosstalk in autostereoscopic display

Perceived crosstalk assessment on patterned retarder 3D display

Quantifying the importance of cyclopean view and binocular rivalry-related features for objective quality assessment of mobile 3D video

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