Intra-Frame Compression of Point Cloud Geometry using Boolean Decomposition

Rosario, Rodrigo; Peixoto, Eduardo

doi:10.1109/vcip47243.2019.8965783

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…When this type of codecs was applied to point cloud geometry slices it was reported that the JBIG standard performed poorly [5]. The main reason is that these slices are much more sparse than the typical binary images.…”

Section: Resultsmentioning

confidence: 99%

“…Pinho [3] uses bi-level image compression to encode the contour maps of a binary image, while Abdat et al [4] proposed a scheme that represents a binary image with a Gray code, then encodes each bit plane separately as a binary image. Recently, some state-of-the-art encoders of point cloud geometry [5], [6] represent the point cloud occupancy map as an array of bi-level images. An example of this representation is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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Context Selection for Lossless Compression of Bi-Level Images

Ramalho¹,

Peixoto²

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Bi-level images are used in many fields to represent binary masks for object and region of interest coding, enhancement layers of color images and point cloud occupancy maps as geometry slices. Compression of bi-level images is usually performed using context adaptive arithmetic coding. In this paper, we propose a low complexity context selection algorithm that chooses, for each image being encoded, the most useful contexts for encoding that particular image. This information is then transmitted in the bitstream at the cost of 16 bits. Results show that the proposed algorithm outperforms the typical, fixed contexts for natural images and, by a larger margin, for point cloud geometry slices.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context Selection for Lossless Compression of Bi-Level Images

Ramalho¹,

Peixoto²

2020

Anais De XXXVIII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

Self Cite

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“…Recently, good lossless performance was achieved by methods based on successive decomposition of the point cloud data into binary images [11,12], without using octree representations.…”

Section: Introductionmentioning

confidence: 99%

Refining The Bounding Volumes for Lossless Compression of Voxelized Point Clouds Geometry

Kaya

Schwarz

Tăbuş

2021

2021 IEEE International Conference on Image Processing (ICIP)

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This paper describes a novel lossless compression method for point cloud geometry, building on a recent lossy compression method that aimed at reconstructing only the bounding volume of a point cloud. The proposed scheme starts by partially reconstructing the geometry from the two depthmaps associated to a single projection direction. The partial reconstruction obtained from the depthmaps is completed to a full reconstruction of the point cloud by sweeping section by section along one direction and encoding the points which were not contained in the two depthmaps. The main ingredient is a list-based encoding of the inner points (situated inside the feasible regions) by a novel arithmetic three dimensional context coding procedure that efficiently utilizes rotational invariances present in the input data. State-of-the-art bits-per-voxel results are obtained on benchmark datasets.

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