167 citations · 278 across the 16 of their papers we have counts for
17 papers
Comp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent
Yixin Gao, Xin Li, Xiaohan Pan +7
We present Comp-X, the first intelligently interactive image compression paradigm empowered by the impressive reasoning capability of large language model (LLM) agent. Notably, com…
UniMIC: Towards Universal Multi-modality Perceptual Image Compression
Yixin Gao, Xin Li, Xiaohan Pan +5
We present UniMIC, a universal multi-modality image compression framework, intending to unify the rate-distortion-perception (RDP) optimization for multiple image codecs simultaneo…
Towards Defining an Efficient and Expandable File Format for AI-Generated Contents
Yixin Gao, Runsen Feng, Xin Li +2
Recently, AI-generated content (AIGC) has gained significant traction due to its powerful creation capability. However, the storage and transmission of large amounts of high-qualit…
End-to-End Rate-Distortion Optimized 3D Gaussian Representation
Henan Wang, Hanxin Zhu, Tianyu He +4
3D Gaussian Splatting (3DGS) has become an emerging technique with remarkable potential in 3D representation and image rendering. However, the substantial storage overhead of 3DGS…
Conditional Neural Video Coding with Spatial-Temporal Super-Resolution
Henan Wang, Xiaohan Pan, Runsen Feng +2
This document is an expanded version of a one-page abstract originally presented at the 2024 Data Compression Conference. It describes our proposed method for the video track of th…
NVTC: Nonlinear Vector Transform Coding
Runsen Feng, Zongyu Guo, Weiping Li +1
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image c…
Exploring the Rate-Distortion-Complexity Optimization in Neural Image Compression
Yixin Gao, Runsen Feng, Zongyu Guo +1
Despite a short history, neural image codecs have been shown to surpass classical image codecs in terms of rate-distortion performance. However, most of them suffer from significan…
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Ruoyu Feng, Yixin Gao, Xin Jin +2
Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applicatio…
Inpaint Anything: Segment Anything Meets Image Inpainting
Tao Yu, Runseng Feng, Ruoyu Feng +4
Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…
Image Coding for Machines with Omnipotent Feature Learning
Ruoyu Feng, Xin Jin, Zongyu Guo +6
Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) a…
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks
Xin Jin, Ruoyu Feng, Simeng Sun +3
Traditional media coding schemes typically encode image/video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream l…
Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
Zongyu Guo, Runsen Feng, Zhizheng Zhang +2
Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussia…
Versatile Learned Video Compression
Runsen Feng, Zongyu Guo, Zhizheng Zhang +1
Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned…
Soft then Hard: Rethinking the Quantization in Neural Image Compression
Zongyu Guo, Zhizheng Zhang, Runsen Feng +1
Quantization is one of the core components in lossy image compression. For neural image compression, end-to-end optimization requires differentiable approximations of quantization,…
Causal Contextual Prediction for Learned Image Compression
Zongyu Guo, Zhizheng Zhang, Runsen Feng +1
Over the past several years, we have witnessed impressive progress in the field of learned image compression. Recent learned image codecs are commonly based on autoencoders, that f…
Learned Video Compression with Feature-level Residuals
Runsen Feng, Yaojun Wu, Zongyu Guo +3
In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the curr…
3-D Context Entropy Model for Improved Practical Image Compression
Zongyu Guo, Yaojun Wu, Runsen Feng +2
In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthene…