HENCE: Hardware End-to-End Neural Conditional Entropy Encoder for Lossless 3D Medical Image Compression
Recently, learning-based lossless compression methods for volumetric medical images have attracted much attention. They can achieve higher compression ratios than traditional methods, albeit at the cost of slower compression speed. Although using field programmable gate arrays (FPGAs) is feasible to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10786994/ |
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| Summary: | Recently, learning-based lossless compression methods for volumetric medical images have attracted much attention. They can achieve higher compression ratios than traditional methods, albeit at the cost of slower compression speed. Although using field programmable gate arrays (FPGAs) is feasible to mitigate this disadvantage, existing FPGA-based compression frameworks still need CPU for co-processing. In this work, we propose a hardware end-to-end neural conditional entropy encoder (HENCE), for losslessly compressing 3D medical images with the balanced compression ratio and speed. To achieve this, we first introduce a context-based entropy model to reduce data redundancy within inter-slice and intra-slice features, using an efficient combination of auto-regressive and recurrent neural networks. Then, we design a hardware-friendly arithmetic coding module to collaborate with our learning-based entropy model. To obtain the cumulative distribution function of the discrete logistic distribution, we further introduce a high-precision Sigmoid approximation algorithm, using the Newton-Raphson method. Finally, we design a dataflow mechanism for the entropy model and the coding module, achieving a fully pipelined compression system. Extensive experimental results show that our method outperforms traditional image/video codecs like FLIF, JPEX-XL, and HEVC on several volumetric medical image datasets. And our method obtains faster encoding speed than existing learning-based medical image compression frameworks. |
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| ISSN: | 2169-3536 |