Adaptive edge-oriented depth image smoothing approach for depth image based rendering

Lee, Pei-Jun; Effendi,

doi:10.1109/isbmsb.2010.5463157

Cited by 21 publications

(11 citation statements)

References 5 publications

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“…Traditionally this problem is resolved using inpainting algorithms such as [15] [16] [17]. Other methods propose a preprocessing of the depth maps in order to reduce the size of disocclusions [18], [19]. When working on video sequences, temporal correlations can also be exploited to retrieve information on disoccluded areas [20] or a background extraction can be performed [21] [22].…”

Section: Introductionmentioning

confidence: 99%

Using region-of-interest for quality evaluation of DIBR-based view synthesis methods

Purica

Valenzise

Pesquet‐Popescu

et al. 2016

2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX)

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Abstract-As 3D media became more and more popular over the last years, new technologies are needed in the transmission, compression and creation of 3D content. One of the most commonly used techniques for aiding with the compression and creation of 3D content is known as view synthesis. The most effective class of view synthesis algorithms are using DepthImage-Based-Rendering techniques, which use explicit scene geometry to render new views. However, these methods may produce geometrical distortions and localized artifacts which are difficult to evaluate as they are inherently different from encoding errors and they are perceived differently by human subjects. In this paper, we propose a region-of-interest evaluation technique for view synthesis based on DIBR methods. Based on the assumption that certain areas determined by the geometrical properties of the scene are prone to distortions, we select a ROI by analyzing the multiple DIBR methods together with the ground truth. The approach is tested using a subjective evaluation view synthesis database and show that our method improves the SSIM correlation with subjective scores We also test another similar method and traditional metrics.

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Section: Introductionmentioning

confidence: 99%

Using region-of-interest for quality evaluation of DIBR-based view synthesis methods

Purica

Valenzise

Pesquet‐Popescu

et al. 2016

2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX)

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“…One is multi-view video(MVV) and the other is multi-view video plus depth(MVD) [1]. In the MVD, only fewer texture videos are coded with corresponding depth videos, and other views can be generated by means of depth image based rendering(DIBR) [2] technique from decoded data. 3D high-efficiency video coding(3D-HEVC) standard [3] for MVD can improve coding performance significantly in comparison with multi-view video coding(MVC) standard [4] by newly developed coding tools.…”

Section: Introductionmentioning

confidence: 99%

A perceptual preprocess method for 3D-HEVC

Shi

Wang

2015

SPIE Proceedings

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A perceptual preprocessing method for 3D-HEVC coding is proposed in the paper. Firstly we proposed a new JND model, which accounts for luminance contrast masking effect, spatial masking effect, and temporal masking effect, saliency characteristic as well as depth information. We utilize spectral residual approach to obtain the saliency map and built a visual saliency factor based on saliency map. In order to distinguish the sensitivity of objects in different depth. We segment each texture frame into foreground and background by a automatic threshold selection algorithm using corresponding depth information, and then built a depth weighting factor. A JND modulation factor is built with a linear combined with visual saliency factor and depth weighting factor to adjust the JND threshold. Then, we applied the proposed JND model to 3D-HEVC for residual filtering and distortion coefficient processing. The filtering process is that the residual value will be set to zero if the JND threshold is greater than residual value, or directly subtract the JND threshold from residual value if JND threshold is less than residual value. Experiment results demonstrate that the proposed method can achieve average bit rate reduction of 15.11%, compared to the original coding scheme with HTM12.1, while maintains the same subjective quality.Keyword:3D high efficient video coding(3D-HEVC), just-noticeable-distortion(JND), depth information, saliency model

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“…The smoothing preprocessing for depth video can reduce the size of hole and the number of cracks. Lee et al [5] proposed an adaptive smoothing filter based on edge direction. It finds and filters the area which will generate the disocclusion according to the edge direction.…”

Section: Introductionmentioning

confidence: 99%

A novel virtual viewpoint merging method based on machine learning

et al. 2014

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In multi-view video system, multiple video plus depth is main data format of 3D scene representation. Continuous virtual views can be generated by using depth image based rendering (DIBR) technique. DIBR process includes geometric mapping, hole filling and merging. Unique weights, inversely proportional to the distance between the virtual and real cameras, are used to merge the virtual views. However, the weights might not the optimal ones in terms of virtual view quality. In this paper, a novel virtual view merging algorithm is proposed. In the proposed algorithm, machine learning method is utilized to establish an optimal weight model. In the model, color, depth, color gradient and sequence parameters are taken into consideration. Firstly, we render the same virtual view from left and right views, and select the training samples by using a threshold. Then, the eigenvalues of the samples are extracted and the optimal merging weights are calculated as training labels. Finally, support vector classifier (SVC) is adopted to establish the model which is used for guiding virtual views rendering. Experimental results show that the proposed method can improve the quality of virtual views for most sequences. Especially, it is effective in the case of large distance between the virtual and real cameras. And compared to the original method of virtual view synthesis, the proposed method can obtain more than 0.1dB gain for some sequences.

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Adaptive edge-oriented depth image smoothing approach for depth image based rendering

Cited by 21 publications

References 5 publications

Using region-of-interest for quality evaluation of DIBR-based view synthesis methods

Using region-of-interest for quality evaluation of DIBR-based view synthesis methods

A perceptual preprocess method for 3D-HEVC

A novel virtual viewpoint merging method based on machine learning

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