Channel-Level Variable Quantization Network for Deep Image Compression
Abstract
Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neural network-based methods treat channel-wise feature maps equally, reducing the flexibility in handling different types of information. In this paper, we propose a channel-level variable quantization network to dynamically allocate more bitrates for significant channels and withdraw bitrates for negligible channels. Specifically, we propose a variable quantization controller. It consists of two key components: the channel importance module, which can dynamically learn the importance of channels during training, and the splitting-merging module, which can allocate different bitrates for different channels. We also formulate the quantizer into a Gaussian mixture model manner. Quantitative and qualitative experiments verify the effectiveness of the proposed model and demonstrate that our method achieves superior performance and can produce much better visual reconstructions.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2020
- DOI:
- 10.48550/arXiv.2007.12619
- arXiv:
- arXiv:2007.12619
- Bibcode:
- 2020arXiv200712619Z
- Keywords:
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- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Computer Vision and Pattern Recognition
- E-Print:
- Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2020