5 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…
Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields
Yixin Gao, Xiaohan Pan, Xin Li +1
The rapid development of AIGC foundation models has revolutionized the paradigm of image compression, which paves the way for the abandonment of most pixel-level transform and codi…
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…
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…