activity
20202025
collaborators

8 papers

cs.CV2025

Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression

Wanhao Ma, Wei Zhang, Shuai Wan +1

Geometry-based point cloud compression (G-PCC), an international standard designed by MPEG, provides a generic framework for compressing diverse types of point clouds while ensurin…

cs.CV2025

RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation

Zhang Chen, Shuai Wan, Siyu Ren +3

One of the main challenges in point cloud compression (PCC) is how to evaluate the perceived distortion so that the codec can be optimized for perceptual quality. Current standard…

cs.MM2024

Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer

Xiao Huo, Junhui Hou, Shuai Wan +1

The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering…

cs.SD2022

ConferencingSpeech 2022 Challenge: Non-intrusive Objective Speech Quality Assessment (NISQA) Challenge for Online Conferencing Applications

Gaoxiong Yi, Wei Xiao, Yiming Xiao +10

With the advances in speech communication systems such as online conferencing applications, we can seamlessly work with people regardless of where they are. However, during online…

cs.CV2022

Light field Rectification based on relative pose estimation

Xiao Huo, Dongyang Jin, Saiping Zhang +1

Hand-held light field (LF) cameras have unique advantages in computer vision such as 3D scene reconstruction and depth estimation. However, the related applications are limited by…

eess.IV2022

DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video

Saiping Zhang, Luis Herranz, Marta Mrak +3

In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to…