271 research outputs found
CARNet:Compression Artifact Reduction for Point Cloud Attribute
A learning-based adaptive loop filter is developed for the Geometry-based
Point Cloud Compression (G-PCC) standard to reduce attribute compression
artifacts. The proposed method first generates multiple Most-Probable Sample
Offsets (MPSOs) as potential compression distortion approximations, and then
linearly weights them for artifact mitigation. As such, we drive the filtered
reconstruction as close to the uncompressed PCA as possible. To this end, we
devise a Compression Artifact Reduction Network (CARNet) which consists of two
consecutive processing phases: MPSOs derivation and MPSOs combination. The
MPSOs derivation uses a two-stream network to model local neighborhood
variations from direct spatial embedding and frequency-dependent embedding,
where sparse convolutions are utilized to best aggregate information from
sparsely and irregularly distributed points. The MPSOs combination is guided by
the least square error metric to derive weighting coefficients on the fly to
further capture content dynamics of input PCAs. The CARNet is implemented as an
in-loop filtering tool of the GPCC, where those linear weighting coefficients
are encapsulated into the bitstream with negligible bit rate overhead.
Experimental results demonstrate significant improvement over the latest GPCC
both subjectively and objectively.Comment: 13pages, 8figure
IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
Point cloud is a crucial representation of 3D contents, which has been widely
used in many areas such as virtual reality, mixed reality, autonomous driving,
etc. With the boost of the number of points in the data, how to efficiently
compress point cloud becomes a challenging problem. In this paper, we propose a
set of significant improvements to patch-based point cloud compression, i.e., a
learnable context model for entropy coding, octree coding for sampling centroid
points, and an integrated compression and training process. In addition, we
propose an adversarial network to improve the uniformity of points during
reconstruction. Our experiments show that the improved patch-based autoencoder
outperforms the state-of-the-art in terms of rate-distortion performance, on
both sparse and large-scale point clouds. More importantly, our method can
maintain a short compression time while ensuring the reconstruction quality.Comment: 12 page
Attribute Artifacts Removal for Geometry-based Point Cloud Compression
Geometry-based point cloud compression (G-PCC) can achieve remarkable
compression efficiency for point clouds. However, it still leads to serious
attribute compression artifacts, especially under low bitrate scenarios. In
this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove
the artifacts of point cloud attributes compressed by G-PCC. We first construct
a graph based on point cloud geometry coordinates and then use the Chebyshev
graph convolutions to extract features of point cloud attributes. Considering
that one point may be correlated with points both near and far away from it, we
propose a multi-scale scheme to capture the short- and long-range correlations
between the current point and its neighboring and distant points. To address
the problem that various points may have different degrees of artifacts caused
by adaptive quantization, we introduce the quantization step per point as an
extra input to the proposed network. We also incorporate a weighted graph
attentional layer into the network to pay special attention to the points with
more attribute artifacts. To the best of our knowledge, this is the first
attribute artifacts removal method for G-PCC. We validate the effectiveness of
our method over various point clouds. Objective comparison results show that
our proposed method achieves an average of 9.74% BD-rate reduction compared
with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective
comparison results present that visual artifacts such as color shifting,
blurring, and quantization noise are reduced
Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions
In this paper, a quality evaluation of three point cloud coding solutions based on machine learning technology is presented, notably, ADLPCC, PCC_GEO_CNN, and PCGC, as well as LUT_SR, which uses multi-resolution Look-Up Tables. Moreover, the MPEG G-PCC was used as an anchor. A set of six point clouds, representing both landscapes and objects were coded using the five encoders at different bit rates, and a subjective test, where the distorted and reference point clouds were rotated in a video sequence side by side, is carried out to assess their performance. Furthermore, the performance of point cloud objective quality metrics that usually provide a good representation of the coded content is analyzed against the subjective evaluation results. The obtained results suggest that some of these metrics fail to provide a good representation of the perceived quality, and thus are not suitable to evaluate some distortions created by machine learning-based solutions. A comparison between the analyzed metrics and the type of represented scene or codec is also presented.This research was funded by the Portuguese FCT-Fundação para
a Ciência e Tecnologia under the project UIDB/50008/2020, PLive
X-0017-LX-20, and by operation Centro-01-0145-FEDER-000019 -
C4 - Centro de Competencias em Cloud Computing.info:eu-repo/semantics/acceptedVersio
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