Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation

Georgiev, Mihail; Gotchev, Atanas

doi:10.1109/dcc.2018.00062

Cited by 3 publications

(2 citation statements)

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“…This scheme achieves lower bitrate at lossless to near-lossless quality range for mono-view coding. Georgiev et al [14] proposed a down-sampling based depth coding scheme, where the misalignments of depth edges are preserved and refined with the help of super-pixel segmentation of the color video. To improve the depth coding efficiency, asymmetric depth coding algorithms [15] [16] were proposed by encoding some of the depth views with reduced resolution and then reconstructing the depth map to the original resolution at the client side with up-sampling.…”

Section: A Related Workmentioning

confidence: 99%

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

Zhang

Zhu

Hamzaoui

et al. 2021

IEEE Trans. on Image Process.

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Section: A Related Workmentioning

confidence: 99%

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

Zhang

Zhu

Hamzaoui

et al. 2021

IEEE Trans. on Image Process.

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“…Previously, we have proposed techniques for depth resampling and 3D fusion for the case of an asymmetric non-confocal V+Z camera setup, where the depth is sensed in low-sensing conditions [43], [44], as well as techniques for near-lossless depth encoding [45], [46]. In the present work, we present a general framework, which addresses both cases.…”

Section: Relation With Previous Workmentioning

confidence: 99%

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

2020

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We present a general framework which can handle different processing stages of the three-dimensional (3D) scene representation referred to as ''view-plus-depth'' (V+Z). The main component of the framework is the relation between the depth map and the super-pixel segmentation of the color image. We propose a hierarchical super-pixel segmentation which keeps the same boundaries between hierarchical segmentation layers. Such segmentation allows for a corresponding depth segmentation, decimation and reconstruction with varying quality and is instrumental in tasks such as depth compression and 3D data fusion. For the latter we utilize a cross-modality reconstruction filter which is adaptive to the size of the refining super-pixel segments. We propose a novel depth encoding scheme, which includes specific arithmetic encoder and handles misalignment outliers. We demonstrate that our scheme is especially applicable for low bit-rate depth encoding and for fusing color and depth data, where the latter is noisy and with lower spatial resolution.

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A Plane Extraction Method for Embedded Computers in Mobile Robots

Junaedy

Masuta

Sawai

et al. 2021

2021 IEEE Symposium Series on Computational Intelligence (SSCI)

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Improved Depth Compression by Depth Downsampling Guided by Color Super-Pixel Refinement Segmentation

Cited by 3 publications

References 1 publication

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

Highly Efficient Multiview Depth Coding Based on Histogram Projection and Allowable Depth Distortion

A General Framework for Depth Compression and Multi-Sensor Fusion in Asymmetric View-Plus-Depth 3D Representation

A Plane Extraction Method for Embedded Computers in Mobile Robots

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