1 code implementation • 21 Apr 2024 • Kang You, Kai Liu, Li Yu, Pan Gao, Dandan Ding
Despite considerable progress being achieved in point cloud geometry compression, there still remains a challenge in effectively compressing large-scale scenes with sparse surfaces.
no code implementations • 21 Jan 2024 • Yichi Zhang, Zhihao Duan, Ming Lu, Dandan Ding, Fengqing Zhu, Zhan Ma
While convolution and self-attention are extensively used in learned image compression (LIC) for transform coding, this paper proposes an alternative called Contextual Clustering based LIC (CLIC) which primarily relies on clustering operations and local attention for correlation characterization and compact representation of an image.
no code implementations • 22 Mar 2023 • Jianqiang Wang, Dandan Ding, Zhan Ma
With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency.
no code implementations • 28 Jan 2023 • Jianqiang Wang, Dandan Ding, Hao Chen, Zhan Ma
This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding.
no code implementations • 17 Sep 2022 • Dandan Ding, Junzhe Zhang, Jianqiang Wang, Zhan Ma
A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts.
2 code implementations • 20 Nov 2021 • Jianqiang Wang, Dandan Ding, Zhu Li, Xiaoxing Feng, Chuntong Cao, Zhan Ma
We call this compression method SparsePCGC.
no code implementations • 1 Dec 2020 • Ming Lu, Tong Chen, Dandan Ding, Fengqing Zhu, Zhan Ma
Inspired by the facts that retinal cells actually segregate the visual scene into different attributes (e. g., spatial details, temporal motion) for respective neuronal processing, we propose to first decompose the input video into respective spatial texture frames (STF) at its native spatial resolution that preserve the rich spatial details, and the other temporal motion frames (TMF) at a lower spatial resolution that retain the motion smoothness; then compress them together using any popular video coder; and finally synthesize decoded STFs and TMFs for high-fidelity video reconstruction at the same resolution as its native input.
3 code implementations • 7 Nov 2020 • Jianqiang Wang, Dandan Ding, Zhu Li, Zhan Ma
Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes.