CLUT

CLUT: Compressed Representation of 3DLUT

Two attempts to compress 3DLUTs via learning: low-rank decomposition and hash. Higher performance with much smaller models! ☺️

CLUT-Net: Learning Adaptively Compressed Representations of 3DLUTs for Lightweight Image Enhancement

• Fengyi Zhang, Hui Zeng, Tianjun Zhang, Lin Zhang
• ACMMM2022

Weak correlations

Learned matrices

3D visualization of the learned basis 3DLUTs (Left: initial identity mapping. Right: after training)

Grid occupancy visualization

All the visualization codes could be found in utils/.

Acknowledgments

• This repo.'s framework and the implementation of CLUTNet are built on the excellent work of Zeng et al: Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time. TPAMI2020

• The multi-resolution HashLUTs are implemented based on the fast hash encoding of NVIDIA Tiny-CUDA-NN.

Great appreciation for the efforts of the above work and all collaborators and for your interest!

Sincerely hope our work helps! ? ?

BibTeX ?

@inproceedings{clutnet,
author = {Zhang, Fengyi and Zeng, Hui and Zhang, Tianjun and Zhang, Lin},
title = {CLUT-Net: Learning Adaptively Compressed Representations of 3DLUTs for Lightweight Image Enhancement},
year = {2022},
isbn = {9781450392037},
url = {https://doi.org/10.1145/3503161.3547879},
doi = {10.1145/3503161.3547879},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {6493–6501},
numpages = {9},
}

@INPROCEEDINGS{hashlut,
author={Zhang, Fengyi and Zhang, Lin and Zhang, Tianjun and Wang, Dongqing},
booktitle={2023 IEEE International Conference on Multimedia and Expo (ICME)}, 
title={Adaptively Hashing 3DLUTs for Lightweight Real-time Image Enhancement}, 
year={2023},
volume={},
number={},
pages={2771-2776},
doi={10.1109/ICME55011.2023.00471}}