35 citations · 220 across the 36 of their papers we have counts for
16 papers · 1 filter
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…
Super-High-Fidelity Image Compression via Hierarchical-ROI and Adaptive Quantization
Jixiang Luo, Yan Wang, Hongwei Qin
Learned Image Compression (LIC) has achieved dramatic progress regarding objective and subjective metrics. MSE-based models aim to improve objective metrics while generative models…
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…
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…
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…
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…
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
Dasong Li, Xiaoyu Shi, Yi Zhang +5
Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame infor…
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…
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network
Dasong Li, Yi Zhang, Ka Lung Law +3
With the growing popularity of smartphones, capturing high-quality images is of vital importance to smartphones. The cameras of smartphones have small apertures and small sensor ce…
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…
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…
Rethinking Noise Synthesis and Modeling in Raw Denoising
Yi Zhang, Hongwei Qin, Xiaogang Wang +1
The lack of large-scale real raw image denoising dataset gives rise to challenges on synthesizing realistic raw image noise for training denoising models. However, the real raw ima…
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…