Multistep joint bilateral depth upsampling

Riemens, A. K.; Gangwal, Om Prakash; Barenbrug, B.; Berretty, Robert-Paul

doi:10.1117/12.805640

Cited by 52 publications

(33 citation statements)

References 17 publications

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“…When q ¼ p, then I p;n does not necessarily have to be equal to I q;nÀ1 and therefore all kernel weights could still underflow. 4 Aliasing artifacts in depth upsampling occur during the range weight computation when the color guidance image is sparsely sampled without being properly prefiltered first [32]. Table 1 ranks for each tested upscale factor U 2 f8; 4; 2g and two simulated noise levels n 2 f0:05; 0:1g, the top 3 methods described in Vosters et al [45] including PWAS-MCM on depth accuracy 5 In contrast to our previous work in [45] where we simulate depth noise as spatially invariant white Gaussian noise, in this work we simulate noise with the time-of-flight (ToF) based noise model of Edeler et al [12].…”

Section: Depth Upsamplingmentioning

confidence: 99%

Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration

Vosters

Varekamp²,

Haan

2015

J Real-Time Image Proc

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Section: Depth Upsamplingmentioning

confidence: 99%

Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration

Vosters

Varekamp²,

Haan

2015

J Real-Time Image Proc

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“…In various works, e.g. [9] and [10], cross-component bi-lateral filtering has been used for depth upsampling. In a cross-component bi-lateral filter, the similarity of co-located texture samples is used to derive filter weights for depth in addition to the conventional filtering window applied spatially for the depth samples.…”

Section: Depth Map Resamplingmentioning

confidence: 99%

“…The pixel values of , , and in Fig. 1 can be calculated using the equations shown in (10). (10) Finally, when , , , and in the BOP of the upsampled image are available, bi-linear interpolation is applied to obtain the remaining pixel values in the BOP.…”

Section: B Upsamplingmentioning

confidence: 99%

Nonlinear Depth Map Resampling for Depth-Enhanced 3-D Video Coding

Aflaki

Hannuksela

Rusanovskyy

et al. 2013

IEEE Signal Process. Lett.

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Abstract-Depth-enhanced 3-D video coding includes coding of texture views and associated depth maps. It has been observed that coding of depth map at reduced resolution provides better rate-distortion performance on synthesized views comparing to utilization of full resolution (FR) depth maps in many coding scenarios based on the Advanced Video Coding (H.264/AVC) standard. Conventional techniques for down and upsampling do not take typical characteristics of depth maps, such as distinct edges and smooth regions within depth objects, into account. Hence, more efficient down and upsampling tools, capable of preserving edges better, are needed. In this letter, novel non-linear methods to down and upsample depth maps are presented. Bitrate comparison of synthesized views, including texture and depth map bitstreams, is presented against a conventional linear resampling algorithm. Objective results show an average bitrate reduction of 5.29% and 3.31% for the proposed down and upsampling methods with ratio ½, respectively, comparing to the anchor method. Moreover, a joint utilization of the proposed down and upsampling brings up to 20% and on average 7.35% bitrate reduction.

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“…Filtering of depth maps has been addressed mainly from the point of view of increasing the resolution [5][6][7]. In [6], a joint bilateral filtering has been suggested to upsample low-resolution depth maps.…”

Section: Introductionmentioning

confidence: 99%

Methods for depth-map filtering in view-plus-depth 3D video representation

Smirnov

Gotchev

Егиазарян

2012

EURASIP J. Adv. Signal Process.

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View-plus-depth is a scene representation format where each pixel of a color image or video frame is augmented by per-pixel depth represented as gray-scale image (map). In the representation, the quality of the depth map plays a crucial role as it determines the quality of the rendered views. Among the artifacts in the received depth map, the compression artifacts are usually most pronounced and considered most annoying. In this article, we study the problem of post-processing of depth maps degraded by improper estimation or by block-transformbased compression. A number of post-filtering methods are studied, modified and compared for their applicability to the task of depth map restoration and post-filtering. The methods range from simple and trivial Gaussian smoothing, to in-loop deblocking filter standardized in H.264 video coding standard, to more comprehensive methods which utilize structural and color information from the accompanying color image frame. The latter group contains our modification of the powerful local polynomial approximation, the popular bilateral filter, and an extension of it, originally suggested for depth super-resolution. We further modify this latter approach by developing an efficient implementation of it. We present experimental results demonstrating high-quality filtered depth maps and offering practitioners options for highest-quality or better efficiency.

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Multistep joint bilateral depth upsampling

Cited by 52 publications

References 17 publications

Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration

Overview of efficient high-quality state-of-the-art depth enhancement methods by thorough design space exploration

Nonlinear Depth Map Resampling for Depth-Enhanced 3-D Video Coding

Methods for depth-map filtering in view-plus-depth 3D video representation

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