325 citations · 986 across the 13 of their papers we have counts for
13 papers
SpeedyGS: Content-Aware 3D Gaussian Splatting Compression via Two-Stage Optimization
Junteng Zhang, Tong Chen, Yuxin Zhao +3
Recent progress in compressing large-scale 3D Gaussian Splatting (3DGS) data has substantially reduced storage footprint, network transmission bandwidth, and memory traffic to GPU…
RENO: Real-Time Neural Compression for 3D LiDAR Point Clouds
Kang You, Tong Chen, Dandan Ding +2
Despite the substantial advancements demonstrated by learning-based neural models in the LiDAR Point Cloud Compression (LPCC) task, realizing real-time compression - an indispensab…
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Long Bai, Tong Chen, Yanan Wu +3
Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufactu…
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning
Tong Chen, Zhan Ma
Deep neural network-based image compression has been extensively studied. However, the model robustness which is crucial to practical application is largely overlooked. We propose…
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning
Ming Lu, Tong Chen, Dandan Ding +2
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,…
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Haojie Liu, Han shen, Lichao Huang +3
Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role i…
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
Tong Chen, Haojie Liu, Zhan Ma +3
This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual networ…
Learned Point Cloud Geometry Compression
Jianqiang Wang, Hao Zhu, Zhan Ma +3
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a.k.a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep…
Non-local Attention Optimized Deep Image Compression
Haojie Liu, Tong Chen, Peiyao Guo +4
This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure. O…
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization
Chao Huang, Haojie Liu, Tong Chen +2
We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate. Our method leverages the "prior…
Gated Context Model with Embedded Priors for Deep Image Compression
Haojie Liu, Tong Chen, Peiyao Guo +2
A deep image compression scheme is proposed in this paper, offering the state-of-the-art compression efficiency, against the traditional JPEG, JPEG2000, BPG and those popular learn…
Neural Video Compression using Spatio-Temporal Priors
Haojie Liu, Tong Chen, Ming Lu +2
The pursuit of higher compression efficiency continuously drives the advances of video coding technologies. Fundamentally, we wish to find better "predictions" or "priors" that are…
Deep Image Compression via End-to-End Learning
Haojie Liu, Tong Chen, Qiu Shen +2
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-sc…