- ๐ Research interests:
HDR (High Dynamic Range), Inverse Tone-mapping, Tone-mapping, WCG (Wide Color Gamut), Gamut Mapping, IQA (Iamge Quality Assessment)
- ๐ซ Concact:
guocheng@cuc.edu.cn, guocheng50655@qq.com, guocheng50655@gmail.com
- ๐ญ Education:
Ph.D. (Expected Jun. 2024) at State Key Laboratory of Media Convergence and Communication (MCC), Communication University of China (CUC), Beijing, China
- ๐ฑ Current occupation:
Visiting student at Peng Cheng Laboratory (PCL), Shenzhen, China
(Project: HDRTVDM; Model: LSN; Dataset: HDRTV4K):
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models
- A luminance segmented network (LSN) AI model with channel decoupled self-attention, for inverse tone-mapping.
- New HDRTV4K training set and test set (SDR-HDR/WCG image pairs).
- New subjective metrics and objective assessment method on inverse tone-mapped HDR/WCG content.
(Model/Algorithm: ITM-LUT, plus an overview of AI-3D-LUT algorithms):
Redistributing the Precision and Content in 3D-LUT-based Inverse Tone-mapping for HDR/WCG Display
An efficient AI inverse tone-mapping for edge devices:
- AI learning of look-up table (LUT) content, and self-adaptability (LUT content will alter with input image) by the AI merging of basic LUTs.
- Run with fewer LUT size on higher-bit-depth (10/12bit) HDR/WCG, by discriminative non-uniform sampling of 3 smaller LUTs.
(Model/Algorithm: LHDR):
LHDR: HDR Reconstruction for Legacy Content Using a Lightweight DNN
- An AI model for single-image HDR reconstruction, with partial convolution and condition.
- Lightweight design using mixed precision of network parameters etc.
Deep Tone-Mapping Operator Using Image Quality Assessment Inspired Semi-Supervised Learning
(Model/Algorithm: IQATM):
- An AI model for tone-mapping, with Laplacian Pyramid decomposition.
- Introducing IQA (image quality assessment) concept and metrics to unsupervised and semi-supervised training.