Adaptive wavelet coding of the depth map for stereoscopic view synthesis

Daribo, Ismael; Tillier, Christophe; Pesquet‐Popescu, Béatrice

doi:10.1109/mmsp.2008.4665114

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…We proceed to discuss how to estimate the two component noise signals in (4). We first focus on the noise caused by lossy coding of texture image.…”

Section: Estimate the Noise Power Due To Texture Codingmentioning

confidence: 99%

“…We trained a model to estimate ρZ (parameterized by the average of the variances of ∆m l and ∆mr), and the same model is used for all sequences and conditions. Finally, E[N 2 ] and E[Z 2 ] are summed to estimate the noise power in the synthesis output following (4).…”

Section: Model Summarymentioning

confidence: 99%

“…The rendering quality, however, depends on several factors and complicated interactions between them. In particular, texture and depth images may contain errors due to imperfect sensing or lossy compression [4,5], and it is not clear how these errors interact and affect the rendering quality. Unlike texture errors, which cause distortion in the luminance/chrominance level, depth errors cause position errors in synthesis [6], and the effect is more subtle.…”

Section: Introductionmentioning

confidence: 99%

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On modeling the rendering error in 3D video

Cheung

Tian

Vetro

et al. 2012

2012 19th IEEE International Conference on Image Processing

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We propose an analytical model to estimate the rendering quality in 3D video. The model relates errors in the depth images to the rendering quality, taking into account texture image characteristics, texture image quality, the camera configuration and the rendering process. Specifically, we derive position (disparity) errors from the depth errors, and the probability distribution of the position errors is used to calculate the power spectral density of the rendering errors. Experiment results with video sequences and coding/ rendering tools used in MPEG 3DV activities show that the model can accurately estimate the synthesis noise up to a constant offset. Thus, the model can be used to estimate the change in rendering quality for different system designs. IEEE International Conference on Image Processing (ICIP)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ON MODELING THE RENDERING ERROR IN 3D VIDEO Ngai-Man Cheung, Dong Tian, Anthony Vetro, Huifang SunMitsubishi Electric Research Laboratories ABSTRACT We propose an analytical model to estimate the rendering quality in 3D video. The model relates errors in the depth images to the rendering quality, taking into account texture image characteristics, texture image quality, the camera configuration and the rendering process. Specifically, we derive position (disparity) errors from the depth errors, and the probability distribution of the position errors is used to calculate the power spectral density of the rendering errors. Experiment results with video sequences and coding/rendering tools used in MPEG 3DV activities show that the model can accurately estimate the synthesis noise up to a constant offset. Thus, the model can be used to estimate the change in rendering quality for different system designs.

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“…We proceed to discuss how to estimate the two component noise signals in (4). We first focus on the noise caused by lossy coding of texture image.…”

Section: Estimate the Noise Power Due To Texture Codingmentioning

confidence: 99%

Section: Model Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On modeling the rendering error in 3D video

Cheung

Tian

Vetro

et al. 2012

2012 19th IEEE International Conference on Image Processing

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“…Concerning coding applications, the proposed DDE raises an important issue: the expensive cost of transmission/storage of the disparity map in terms of bitrate [25]. In order to reduce this cost, we propose to apply a quadtree decomposition (QTD) to the estimated field vx.…”

Section: Dense Disparity Map Codingmentioning

confidence: 99%

Dense disparity map representations for stereo image coding

Kaaniche

Miled

Pesquet‐Popescu

et al. 2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Marne-la-Vallée, France pesquet@univ-mlv.fr ABSTRACTResearch in stereo image coding has focused on the disparity estimation/compensation process to exploit the cross-view redundancies.Most of the reported methods use a classical block-based technique in order to estimate the disparity field. However, this estimation technique does not always provide an accurate disparity map, which may affect the disparity compensation step. In this paper, we propose to use an estimation method that produces a dense and smooth disparity map. Then, on the one hand, this map is segmented and efficiently coded by exploiting the high correlation between neighboring disparity values. On the other hand, we integrate the disparity information into a vector lifting scheme for stereo image coding. Experimental results indicate that the proposed coding scheme outperforms the conventional methods employing a block-based disparity estimation.Index Terms-disparity estimation, dense disparity map, stereoscopic image coding, vector lifting scheme.

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“…The synthesis quality, however, depends on several factors and complicated interactions between them. In particular, texture and depth images may contain errors due to imperfect sensing or lossy compression [8], [9], and it is not clear how these errors interact and affect the rendering quality. Unlike texture errors, which cause distortion in the luminance/chrominance level, depth errors cause position errors in synthesis [10], i.e., pixels are warped to slightly shifted positions during synthesis.…”

Section: Introductionmentioning

confidence: 99%

Synthesis distortion estimation in 3D video using frequency and spatial analysis

Cheung

Tian

Vetro

et al. 2013

2013 IEEE International Conference on Image Processing

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We propose an analytical model to estimate the synthesized view quality in 3D video. Specifically, we estimate the depth-error induced distortion using an approach that combines frequency and spatial domain analysis. We also propose to decompose the spatial-variant video signals into gradient-based representations to capture the interaction between image gradients, depth errors and synthesis distortion. Experiment results with video sequences and coding/rendering tools used in MPEG 3DV activities show that our analytical model can accurately estimate the synthesis noise power. IEEE International Conference on Image Processing (ICIP)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. ABSTRACT We propose an analytical model to estimate the synthesized view quality in 3D video. Specifically, we estimate the depth-error induced distortion using an approach that combines frequency and spatial domain analysis. We also propose to decompose the spatial-variant video signals into gradient-based representations to capture the interaction between image gradients, depth errors and synthesis distortion. Experiment results with video sequences and coding/rendering tools used in MPEG 3DV activities show that our analytical model can accurately estimate the synthesis noise power.

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Adaptive wavelet coding of the depth map for stereoscopic view synthesis

Cited by 32 publications

References 11 publications

On modeling the rendering error in 3D video

On modeling the rendering error in 3D video

Dense disparity map representations for stereo image coding

Synthesis distortion estimation in 3D video using frequency and spatial analysis

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