| --- |
| license: apache-2.0 |
| size_categories: |
| - 100K<n<1M |
| --- |
| ## Abstract |
| The latent representation in learned image compression encompasses channel-wise, local spatial, and global spatial correlations, which are essential for the entropy model to capture for conditional entropy minimization. Efficiently capturing these contexts within a single entropy model, especially in high-resolution image coding, presents a challenge due to the computational complexity of existing global context modules. To address this challenge, we propose the Linear Complexity Multi-Reference Entropy Model (MEM++). Specifically, the latent representation is partitioned into multiple slices. For channel-wise contexts, previously compressed slices serve as the context for compressing a particular slice. For local contexts, we introduce a shifted-window-based checkerboard attention module. This module ensures linear complexity without sacrificing performance. For global contexts, we propose a linear complexity attention mechanism. It captures global correlations by decomposing the softmax operation, enabling the implicit computation of attention maps from previously decoded slices. Using MEM++ as the entropy model, we develop the image compression method MLIC++. Extensive experimental results demonstrate that MLIC++ achieves state-of-the-art performance, reducing BD-rate by 13.39% on the Kodak dataset compared to VTM-17.0 in Peak Signal-to-Noise Ratio (PSNR). Furthermore, MLIC++ exhibits linear computational complexity and memory consumption with resolution, making it highly suitable for high-resolution image coding. Code and pre-trained models are available at https://github.com/JiangWeibeta/MLIC. Training dataset is available at https://huggingface.co/datasets/Whiteboat/MLIC-Train-100K. |
|
|
| ## Dataset |
| This dataset is compressed into **37** volumes using 7z. |
|
|
| ## Citation |
| If you use this dataset (MLIC-Train-100K) in your research or project: |
| 1. Please kindly **Mention the Dataset** by including the name "MLIC-Train-100K" in your paper. |
| 2. Please kindly **Cite our MLIC, MLIC++ and MLICv2** using the following reference. |
| |
| Thank you! |
|
|
| #### MLIC |
|
|
| ``` |
| @inproceedings{jiang2023mlic, |
| title={MLIC: Multi-Reference Entropy Model for Learned Image Compression}, |
| author={Jiang, Wei and Yang, Jiayu and Zhai, Yongqi and Ning, Peirong and Gao, Feng and Wang, Ronggang}, |
| doi = {10.1145/3581783.3611694}, |
| booktitle={Proceedings of the 31st ACM International Conference on Multimedia}, |
| pages={7618--7627}, |
| year={2023} |
| } |
| ``` |
|
|
| #### MLIC <sup> ++ </sup> |
|
|
| ``` |
| @inproceedings{jiang2023mlicpp, |
| title={MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression}, |
| author={Jiang, Wei and Wang, Ronggang}, |
| booktitle={ICML 2023 Workshop Neural Compression: From Information Theory to Applications}, |
| year={2023}, |
| url={https://openreview.net/forum?id=hxIpcSoz2t} |
| } |
| ``` |
|
|
| #### MLICv2 |
| ``` |
| @article{jiang2025mlicv2, |
| title={MLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression}, |
| author={Jiang, Wei and Zhai, Yongqi and Yang, Jiayu and Gao, Feng and Wang, Ronggang}, |
| journal={arXiv preprint arXiv:2504.19119}, |
| year={2025} |
| } |
| ``` |
