35 citations · 89 across the 23 of their papers we have counts for
24 papers
GaussianImage++: Boosted Image Representation and Compression with 2D Gaussian Splatting
Tiantian Li, Xinjie Zhang, Xingtong Ge +4
Implicit neural representations (INRs) have achieved remarkable success in image representation and compression, but they require substantial training time and memory. Meanwhile, r…
Fast Training-free Perceptual Image Compression
Ziran Zhu, Tongda Xu, Minye Huang +5
Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely…
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Tongda Xu, Xiyan Cai, Xinjie Zhang +7
Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is…
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
Xinjie Zhang, Zhening Liu, Yifan Zhang +7
4D Gaussian Splatting (4DGS) has recently emerged as a promising technique for capturing complex dynamic 3D scenes with high fidelity. It utilizes a 4D Gaussian representation and…
Task-Aware Encoder Control for Deep Video Compression
Xingtong Ge, Jixiang Luo, Xinjie Zhang +7
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task. In c…
Boosting Neural Representations for Videos with a Conditional Decoder
Xinjie Zhang, Ren Yang, Dailan He +5
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks. However, ex…
Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers
Tongda Xu, Ziran Zhu, Jian Li +8
Diffusion Inverse Solvers (DIS) are designed to sample from the conditional distribution , with a predefined diffusion model , an operator , and a m…
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
Xinjie Zhang, Xingtong Ge, Tongda Xu +6
Implicit neural representations (INRs) recently achieved great success in image representation and compression, offering high visual quality and fast rendering speeds with 10-1000…
CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Xinjie Zhang, Shenyuan Gao, Zhening Liu +6
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations. However,…
Idempotence and Perceptual Image Compression
Tongda Xu, Ziran Zhu, Dailan He +8
Idempotence is the stability of image codec to re-compression. At the first glance, it is unrelated to perceptual image compression. However, we find that theoretically: 1) Conditi…
Efficient Learned Lossless JPEG Recompression
Lina Guo, Yuanyuan Wang, Tongda Xu +6
JPEG is one of the most popular image compression methods. It is beneficial to compress those existing JPEG files without introducing additional distortion. In this paper, we propo…
Conditional Perceptual Quality Preserving Image Compression
Tongda Xu, Qian Zhang, Yanghao Li +7
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information. Specificall…
KBNet: Kernel Basis Network for Image Restoration
Yi Zhang, Dasong Li, Xiaoyu Shi +5
How to aggregate spatial information plays an essential role in learning-based image restoration. Most existing CNN-based networks adopt static convolutional kernels to encode spat…
Multi-Sample Training for Neural Image Compression
Tongda Xu, Yan Wang, Dailan He +4
This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (sota) methods adopt uniform posterior to approximate quantization noise, and sin…
Flexible Neural Image Compression via Code Editing
Chenjian Gao, Tongda Xu, Dailan He +2
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance. However, it usually requires a dedicated encoder-decoder pair for eac…
Bit Allocation using Optimization
Tongda Xu, Han Gao, Chenjian Gao +10
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC). First, we reveal a fundamental relationship between bit allocation in NVC and Semi-Amort…
Evaluating the Practicality of Learned Image Compression
Hongjiu Yu, Qiancheng Sun, Jin Hu +9
Learned image compression has achieved extraordinary rate-distortion performance in PSNR and MS-SSIM compared to traditional methods. However, it suffers from intensive computation…
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
Dailan He, Ziming Yang, Hongjiu Yu +7
In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate re…
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain
Lina Guo, Xinjie Shi, Dailan He +4
JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems. However, most recent progress on image compression mainly…
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Dailan He, Ziming Yang, Weikun Peng +3
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-s…
Post-Training Quantization for Cross-Platform Learned Image Compression
Dailan He, Ziming Yang, Yuan Chen +3
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications. One of the most cri…
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Baocheng Sun, Meng Gu, Dailan He +3
Learned image compression is making good progress in recent years. Peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) are the two most popular evalua…
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
Dailan He, Yusheng Zhao, Junyu Luo +4
Recently proposed fine-grained 3D visual grounding is an essential and challenging task, whose goal is to identify the 3D object referred by a natural language sentence from other…
Checkerboard Context Model for Efficient Learned Image Compression
Dailan He, Yaoyan Zheng, Baocheng Sun +2
For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. Because it helps remove spatial redundancies…