4,563 research outputs found
Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression
As being one of the main representation formats of 3D real world and
well-suited for virtual reality and augmented reality applications, point
clouds have gained a lot of popularity. In order to reduce the huge amount of
data, a considerable amount of research on point cloud compression has been
done. However, given a target bit rate, how to properly choose the color and
geometry quantization parameters for compressing point clouds is still an open
issue. In this paper, we propose a rate-distortion model based quantization
parameter selection scheme for bit rate constrained point cloud compression.
Firstly, to overcome the measurement uncertainty in evaluating the distortion
of the point clouds, we propose a unified model to combine the geometry
distortion and color distortion. In this model, we take into account the
correlation between geometry and color variables of point clouds and derive a
dimensionless quantity to represent the overall quality degradation. Then, we
derive the relationships of overall distortion and bit rate with the
quantization parameters. Finally, we formulate the bit rate constrained point
cloud compression as a constrained minimization problem using the derived
polynomial models and deduce the solution via an iterative numerical method.
Experimental results show that the proposed algorithm can achieve optimal
decoded point cloud quality at various target bit rates, and substantially
outperform the video-rate-distortion model based point cloud compression
scheme.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technolog
Geometry Compression of 3D Static Point Clouds based on TSPLVQ
International audienceIn this paper, we address the challenging problem of the 3D point cloud compression required to ensure efficient transmission and storage. We introduce a new hierarchical geometry representation based on adaptive Tree-Structured Point-Lattice Vector Quantization (TSPLVQ). This representation enables hierarchically structured 3D content that improves the compression performance for static point cloud. The novelty of the proposed scheme lies in adaptive selection of the optimal quantization scheme of the geometric information, that better leverage the intrinsic correlations in point cloud. Based on its adaptive and multiscale structure, two quantization schemes are dedicated to project recursively the 3D point clouds into a series of embedded truncated cubic lattices. At each step of the process, the optimal quantization scheme is selected according to a rate-distortion cost in order to achieve the best trade-off between coding rate and geometry distortion, such that the compression flexibility and performance can be greatly improved. Experimental results show the interest of the proposed multi-scale method for lossy compression of geometry
Aggressive saliency-aware point cloud compression
The increasing demand for accurate representations of 3D scenes, combined
with immersive technologies has led point clouds to extensive popularity.
However, quality point clouds require a large amount of data and therefore the
need for compression methods is imperative. In this paper, we present a novel,
geometry-based, end-to-end compression scheme, that combines information on the
geometrical features of the point cloud and the user's position, achieving
remarkable results for aggressive compression schemes demanding very small bit
rates. After separating visible and non-visible points, four saliency maps are
calculated, utilizing the point cloud's geometry and distance from the user,
the visibility information, and the user's focus point. A combination of these
maps results in a final saliency map, indicating the overall significance of
each point and therefore quantizing different regions with a different number
of bits during the encoding process. The decoder reconstructs the point cloud
making use of delta coordinates and solving a sparse linear system. Evaluation
studies and comparisons with the geometry-based point cloud compression (G-PCC)
algorithm by the Moving Picture Experts Group (MPEG), carried out for a variety
of point clouds, demonstrate that the proposed method achieves significantly
better results for small bit rates
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