Visual comfort enhancement in stereoscopic 3D images using saliency-adaptive nonlinear disparity mapping

Jung, Cheolkon; Chen, Lihui; Liu, Hong‐Min; Kim, Joongkyu

doi:10.1016/j.displa.2015.05.006

Cited by 16 publications

(12 citation statements)

References 18 publications

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“…To comprehensively evaluate the performance of the proposed approach, we select three existing schemes for comparison: 1) Lei's scheme [13] that adjusts the disparity range by controlling the ZDP; 2) Jung's scheme [18] that constructs a saliency-adaptive nonlinear disparity mapping function; 3) our previous scheme [14] that adjusts the ZDP for projection.…”

Section: Resultsmentioning

confidence: 99%

“…In Sohn et al's series of works, various visual comfort improvement methods are proposed based on linear and nonlinear disparity remapping processes jointly to mitigate global and local discomfort causes in [15], by adjusting (scaled and shifted) the disparity of a scene under the guidance of an objective VCA metric in [16], and based on a nonlinear remapping operator to adjust the disparity range according to the determined visual fatigue score in [17]. Jung et al [18] proposed a saliency-adaptive nonlinear disparity mapping based on a sigmoid function to minimize disparity distortions. Other relevant works can also be found in [19,20].…”

Section: Related Workmentioning

confidence: 99%

“…Towards this end, state-of-the-art visual comfort enhancement methods follow two brand categories: disparity shifting [13,14] and disparity remapping [15][16][17][18][19][20]. The disparity shifting methods is comparatively simply that adjust the zero disparity plane (ZDP) of a scene but maintain its overall disparity range.…”

Section: Related Workmentioning

confidence: 99%

“…3) Disparity remapping aims to (linearly or non-linearly) scale the disparity range into a comfort zone to avoid excessive AV conflicts [15][16][17][18][19][20]. However, since the comfort zone range is limited, if large disparity range scaling is conducted, rendering artifacts will be appeared in the processed images.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals

Shao

Jiang

et al. 2016

Opt. Express

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Visual comfort is a long-facing problem in stereoscopic 3D (S3D) display. In this paper, targeting to produce S3D content based on color-plus-depth signals, a general framework for depth mapping to optimize visual comfort for S3D display is proposed. The main motivation of this work is to remap the depth range of color-plus-depth signals to a new depth range that is suitable to comfortable S3D display. Towards this end, we first remap the depth range globally based on the adjusted zero disparity plane, and then present a two-stage global and local depth optimization solution to solve the visual comfort problem. The remapped depth map is used to generate the S3D output. We demonstrate the power of our approach on perceptually uncomfortable and comfortable stereoscopic images.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals

Shao

Jiang

et al. 2016

Opt. Express

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“…In this case, saliency maps are compared with the disparity (depth) maps to provide objective metrics for discomfort. A second use of saliency models is in the enhancement of the viewer comfort as in [58,59] where the blurring of the image is done according to the saliency of the areas.…”

Section: Quality In Stereoscopic 3d Imagesmentioning

confidence: 99%

Applications of Saliency Models

Mancaş¹,

Meur²

2016

From Human Attention to Computational Attention

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International audienceThis chapter proposes a taxonomy to classify the real-life applications which can benefit from the use of attention models. There are numerous applications and we try here to remain as exhaustive as possible to provide a picture of all the applications of saliency models, but also to detect where future developments might be of interest. The applications are grouped into three categories. The first one uses the detection of the most important regions in an image and contains applications such as video surveillance, audio surveillance, defect detection, pathology detection, expressive and social gestures, computer graphics and quality metrics. The second category uses saliency maps to detect the regions which are the less interesting in an image. Here one can found applications like texture metrics, compression, retargeting, summarization, watermarking and attention-based ad insertion. Finally, a third category uses the most interesting areas in an image with further processing like comparisons between those areas. In this category one can find image registration and landmarks, object recognition, action guidance in robotics or avatars, web sites optimization, images memorability, best viewpoint, symmetries and automatic focus on images

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RETRACTED ARTICLE: Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

Rakesh

Krishna

2018

Soft Comput

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Visual comfort enhancement in stereoscopic 3D images using saliency-adaptive nonlinear disparity mapping

Cited by 16 publications

References 18 publications

Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals

Optimizing visual comfort for stereoscopic 3D display based on color-plus-depth signals

Applications of Saliency Models

RETRACTED ARTICLE: Saliency detection in stereoscopic images using adaptive Gaussian Kernel and Gabor filter

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