Edge-preserving depth-map coding using graph-based wavelets

Sánchez, Alfonso Corbacho; Shen, Godwin; Ortega, Antonio

doi:10.1109/acssc.2009.5469898

Cited by 32 publications

(16 citation statements)

References 12 publications

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“…Figure 2(a) shows the UPA proposed in [20], [22], [23] for graph trees: nodes at odd depth are assigned to P, and nodes at even depth are assigned to U. This even/odd assignment has been also employed for lifting-based compact image representation (Figure 2(b)): pixels along odd rows or columns are assigned to P [24]- [26]. Figure 2(c) shows a UPA proposed for liftingbased video coding, where even frames are used to predict the odd frames [27]- [29], and Figure 2(d) shows a UPA for a brain computer interface (BCI) application, where samples at odd time stamps are assigned to P [30], [31].…”

Section: A Motivationmentioning

confidence: 99%

Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Martinez-Enriquez

Cid-Sueiro

Díaz-de-María

et al. 2018

IEEE Trans. Signal Process.

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Abstract-Transformations on graphs can provide compact representations of signals with many applications in denoising, feature extraction or compression. In particular, lifting transforms have the advantage of being critically sampled and invertible by construction, but the efficiency of the transform depends on the choice of a good bipartition of the graph into update (U) and prediction (P) nodes. This is the update/prediction (U/P) assignment problem, which is the focus of this paper. We analyze this problem theoretically and derive an optimal U/P assignment under assumptions about signal model and filters. Furthermore, we prove that the best U/P partition is related to the correlation between nodes on the graph and is not the one that minimizes the number of conflicts (connections between nodes of same label) or maximizes the weight of the cut. We also provide experimental results in randomly generated graph signals and real data from image and video signals that validate our theoretical conclusions, demonstrating improved performance over state of the art solutions for this problem.

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Section: A Motivationmentioning

confidence: 99%

Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Martinez-Enriquez

Cid-Sueiro

Díaz-de-María

et al. 2018

IEEE Trans. Signal Process.

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“…We use the edge detection technique in [9], where the presence of edges between every pixel and its immediate four (or eight) neighbors is provided by an edge map in full (or half)-pixel positions. The edges of the original depth map, which we refer to as the HR edges, are then down-sampled to LR edges with the same downsampling rate as its corresponding pixel block, so that the LR edges can be used for definition of LR GBT for n × n block.…”

Section: Edge Detection and Down-samplingmentioning

confidence: 99%

Depth map compression using multi-resolution graph-based transform for depth-image-based rendering

Cheung

et al. 2012

2012 19th IEEE International Conference on Image Processing

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Depth map compression is important for efficient network transmission of 3D visual data in texture-plus-depth format, where the observer can synthesize an image of a freely chosen viewpoint via depth-image-based rendering (DIBR) using received neighboring texture and depth maps as anchors. Unlike texture maps, depth maps exhibit unique characteristics like smooth interior surfaces and sharp edges that can be exploited for coding gain. In this paper, we propose a multi-resolution approach to depth map compression using previously proposed graph-based transform (GBT). The key idea is to treat smooth surfaces and sharp edges of large code blocks separately and encode them in different resolutions: encode edges in original high resolution (HR) to preserve sharpness, and encode smooth surfaces in low-pass-filtered and down-sampled low resolution (LR) to save coding bits. Because GBT does not filter across edges, it produces small or zero high-frequency components when coding smooth-surface depth maps and leads to a compact representation in the transform domain. By encoding down-sampled surface regions in LR GBT, we achieve representation compactness for a large block without the high computation complexity associated with an adaptive large-block GBT. At the decoder, encoded LR surfaces are up-sampled and interpolated while preserving encoded HR edges. Experimental results show that our proposed multi-resolution approach using GBT reduced bitrate by 68% compared to native H.264 intra with DCT encoding original HR depth maps, and by 55% compared to single-resolution GBT encoding small blocks.

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“…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%

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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Edge-preserving depth-map coding using graph-based wavelets

Cited by 32 publications

References 12 publications

Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Optimized Update/Prediction Assignment for Lifting Transforms on Graphs

Depth map compression using multi-resolution graph-based transform for depth-image-based rendering

On modeling the rendering error in 3D video

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