In the field of medical image compression, Implicit Neural Representation
(INR) networks have shown remarkable versatility due to their flexible
compression ratios, yet they are constrained by a one-to-one fitting approach
that results in lengthy encoding times. Our novel method,
``\textbf{UniCompress}'', innovatively extends the compression capabilities of
INR by being the first to compress multiple medical data blocks using a single
INR network. By employing wavelet transforms and quantization, we introduce a
codebook containing frequency domain information as a prior input to the INR
network. This enhances the representational power of INR and provides
distinctive conditioning for different image blocks. Furthermore, our research
introduces a new technique for the knowledge distillation of implicit
representations, simplifying complex model knowledge into more manageable
formats to improve compression ratios. Extensive testing on CT and electron
microscopy (EM) datasets has demonstrated that UniCompress outperforms
traditional INR methods and commercial compression solutions like HEVC,
especially in complex and high compression scenarios. Notably, compared to
existing INR techniques, UniCompress achieves a 4$\sim$5 times increase in
compression speed, marking a significant advancement in the field of medical
image compression. Codes will be publicly available.

通过使用一种名为 ``UniCompress'' 的新方法，我们扩展了医学图像压缩领域的 Implicit Neural Representation（INR）网络的压缩能力，该方法首次使用一个 INR 网络压缩多个医学数据块。我们的研究引入了一种新的基于波变换和量化的技术，该技术将频域信息作为先验输入到 INR 网络中，并简化了复杂模型知识以提高压缩比。在 CT 和电子显微镜（EM）数据集的大量测试中，我们证明了 UniCompress 在复杂和高压缩场景中的性能优于传统 INR 方法和商业压缩解决方案。值得注意的是，与现有的 INR 技术相比，UniCompress 在压缩速度上实现了 4 到 5 倍的增加，标志着医学图像压缩领域的重大进展。代码将公开使用。