Depth map quality metric for three-dimensional video

Kim, Dong-Hyun; Min, Dongbo; Oh, Juhyun; Jeon, Seong-Gyu; Sohn, Kwanghoon

doi:10.1117/12.806898

Cited by 33 publications

(17 citation statements)

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“…depth discontinuity pixels occur at the right of occluded pixels in the left image and at the left of occluded pixels in the right image in stereoscopy. (1,0), (2,1), (6,2), (7,3), (8,4), (9,5), (10,6), (11,9), (12,10), (13.11…”

Section: A Pixel To Pixel Depth Discontinuitiesmentioning

confidence: 99%

The Extraction of Depth Discontinuities Using Disparity Map for Human Visual Fatigue

So¹,

Kim²,

Kim³

2014

IJCTE

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Abstract-The complexity of the world is a factor of evaluating the visual fatigue of 3D video. Depth discontinuities are the layers of the world and can be a characteristic of the complexity of the world. We suppose a new method to extract a depth discontinuities from a disparity map. We employed DIP algorithm for extraction of the depth discontinuities with a learned threshold automatically inferred based on the average and standard deviation of the disparity, that is, depth of the stereoscopy.Index Terms-Depth discontinuities, DIP, disparity map, human visual fatigue.

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Section: A Pixel To Pixel Depth Discontinuitiesmentioning

confidence: 99%

The Extraction of Depth Discontinuities Using Disparity Map for Human Visual Fatigue

So¹,

Kim²,

Kim³

2014

IJCTE

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“…12. In order to obtain an objective evaluation of the estimated disparity map quality, the estimated disparity map quality metric suggested in [28] is used for comparisons. Figure 12a shows that the estimated disparities of the proposed method have smaller disparities in the left half of the histogram, representing less visual fatigue.…”

Section: Performance Comparisonsmentioning

confidence: 99%

Robust stereo matching with trinary cross color census and triple image-based refinements

Chang

Yang

2017

EURASIP J. Adv. Signal Process.

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For future 3D TV broadcasting systems and navigation applications, it is necessary to have accurate stereo matching which could precisely estimate depth map from two distanced cameras. In this paper, we first suggest a trinary cross color (TCC) census transform, which can help to achieve accurate disparity raw matching cost with low computational cost. The two-pass cost aggregation (TPCA) is formed to compute the aggregation cost, then the disparity map can be obtained by a range winner-take-all (RWTA) process and a white hole filling procedure. To further enhance the accuracy performance, a range left-right checking (RLRC) method is proposed to classify the results as correct, mismatched, or occluded pixels. Then, the image-based refinements for the mismatched and occluded pixels are proposed to refine the classified errors. Finally, the image-based cross voting and a median filter are employed to complete the fine depth estimation. Experimental results show that the proposed semi-global stereo matching system achieves considerably accurate disparity maps with reasonable computation cost.

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“…10. In order to obtain an objective evaluation of the enhanced depth map quality, the enhanced depth map quality metric suggested in [41] is used for comparisons. In Fig.…”

Section: Depth Enhancement With Rcr Processmentioning

confidence: 99%

Robust depth enhancement and optimization based on advanced multilateral filters

Chang

Chou

Yang

2017

EURASIP J. Adv. Signal Process.

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Stereo matching of two distanced cameras and structured-light RGB-D cameras are the two common ways to capture the depth map, which conveys the per-pixel depth information of the image. However, the results with mismatched and occluded pixels would not provide accurately well-matched depth and image information. The mismatched depth-image relations would degrade the performances of view syntheses seriously in modern-day three-dimension video applications. Therefore, how to effectively utilize the image and depth to enhance themselves becomes more and more important. In this paper, we propose an advanced multilateral filter (AMF), which refers spatial, range, depth, and credibility information to achieve their enhancements. The AMF enhancements could sharpen the image, suppress noisy depth, filling depth holes, and sharpen the depth edges simultaneously. Experimental results demonstrate that the proposed method provides a superior performance, especially around the object boundary.

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Depth map quality metric for three-dimensional video

Cited by 33 publications

References 0 publications

The Extraction of Depth Discontinuities Using Disparity Map for Human Visual Fatigue

The Extraction of Depth Discontinuities Using Disparity Map for Human Visual Fatigue

Robust stereo matching with trinary cross color census and triple image-based refinements

Robust depth enhancement and optimization based on advanced multilateral filters

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