Iterative Removal of G-PCC Attribute Compression Artifacts Based on a Graph Neural Network
Zhouyan He,
Wenming Yang,
Lijun Li
et al.
Abstract:As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information may lead to spatial detail loss and visible artifacts, which negatively impact visual quality. To address these challenges, this paper proposes an iterative removal method for attribute compression artifacts base… Show more
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