5,842 research outputs found
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
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Omnidirectional video enables spherical stimuli with the viewing range. Meanwhile, only the viewport region of omnidirectional
video can be seen by the observer through head movement (HM), and an even
smaller region within the viewport can be clearly perceived through eye
movement (EM). Thus, the subjective quality of omnidirectional video may be
correlated with HM and EM of human behavior. To fill in the gap between
subjective quality and human behavior, this paper proposes a large-scale visual
quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which
collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset
provides not only the subjective quality scores of sequences but also the HM
and EM data of subjects. By mining our dataset, we find that the subjective
quality of omnidirectional video is indeed related to HM and EM. Hence, we
develop a deep learning model, which embeds HM and EM, for objective VQA on
omnidirectional video. Experimental results show that our model significantly
improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201
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