End-to-End Learnt Image Compression via Non-Local Attention Optimization and Improved Context Modeling
By Tong Chen, Zhan Ma, Qiu Shen, Xun Cao, Yao Wang. IEEE Transactions on Image Processing, 2019
325 citations recorded by the source corpus.
This article proposes an end-to-end learnt lossy image compression approach, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure with Non-Local Attention optimization and Improved Context modeling (NLAIC). Our NLAIC 1) embeds non-local network operations as non-linear transforms in both main and hyper coders for deriving respective latent features and hyperpriors by exploiting both local and global correlations, 2) applies attention mechanism to generate implicit masks that are used to weigh the features for adaptive bit allocation, and 3) implements the improved conditional entropy modeling of latent features using joint 3D convolutional neural network (CNN)-based autoregressive contexts and hyperpriors. Towards the practical application, additional enhancements are also introduced to speed up the computational processing (e.g., parallel 3D CNN-based context prediction), decrease the memory consumption (e.g., sparse non-local processing) and reduce the implementation complexity (e.g., a unified model for variable rates without re-training). The proposed model outperforms existing learnt and conventional (e.g., BPG, JPEG2000, JPEG) image compression methods, on both Kodak and Tecnick datasets with the state-of-the-art compression efficiency, for both PSNR and MS-SSIM quality measurements. We have made all materials publicly accessible at https://njuvision.github.io/NIC for reproducible research.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/f4ed51903ef3aabb38292cdfbaf634dc232bddc2
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Towards Backward-Compatible Continual Learning of Image Compression (2024, background): “A major group focuses on designing expressive architectures for !enc and f ctec, such as convolutional and transformer-based ones [10, 11, 14, 20, 23, 27, 33, 37-39, 43, 52, 54, 59].”
- QARV: Quantization-Aware ResNet VAE for Lossy Image Compression (2023, background): “To improve compression efficiency, existing works have explored efficient neural network blocks, such as residual networks [21], self-attention [22], and transformers [23], [24], [25], as well as developed more expressive entropy models, such as hierarchical [7], [26] and autoregressive models [8],…”
- ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding (2022, unknown): “[11], as a part of non-local attention module.”