MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917046309093376 |
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| author | Jiang, Wei Yang, Jiayu Zhai, Yongqi Gao, Feng Wang, Ronggang |
| author_facet | Jiang, Wei Yang, Jiayu Zhai, Yongqi Gao, Feng Wang, Ronggang |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2307_15421 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression Jiang, Wei Yang, Jiayu Zhai, Yongqi Gao, Feng Wang, Ronggang Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2307.15421 |