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。

UniCompress：基于知识蒸馏的多数据医学图像压缩增强

UniCompress: Enhancing Multi-Data Medical Image Compression with  Knowledge Distillation

Remote medical diagnosis has emerged as a critical and indispensable
technique in practical medical systems, where medical data are required to be
efficiently compressed and transmitted for diagnosis by either professional
doctors or intelligent diagnosis devices. In this process, a large amount of
redundant content irrelevant to the diagnosis is subjected to high-fidelity
coding, leading to unnecessary transmission costs. To mitigate this, we propose
diagnosis-oriented medical image compression, a special semantic compression
task designed for medical scenarios, targeting to reduce the compression cost
without compromising the diagnosis accuracy. However, collecting sufficient
medical data to optimize such a compression system is significantly expensive
and challenging due to privacy issues and the lack of professional annotation.
In this study, we propose DMIC, the first efficient transfer learning-based
codec, for diagnosis-oriented medical image compression, which can be
effectively optimized with only few-shot annotated medical examples, by reusing
the knowledge in the existing reinforcement learning-based task-driven semantic
coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the
partial parameters of the policy network for bit allocation within HRLVSC,
which enables it to adapt to the medical images. In this work, we validate our
DMIC with the typical medical task, Coronary Artery Segmentation. Extensive
experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings
compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of
the policy network with only 1 medical sample.

远程医疗诊断对于实际医疗系统至关重要，本研究提出了一种面向诊断的医学图像压缩的有效转移学习编码器，通过调整部分策略网络参数以适应医学图像，实验证明仅使用 1 个医学样本，我们的压缩系统可以与 HEVC 参考标准相比节省 47.594% 的比特率，达到 47.594% 的比特率节省。

面向诊断的高效传统学习医学图像压缩

Diagnosis-oriented Medical Image Compression with Efficient Transfer  Learning

Medical image compression is a widely studied field of data processing due to
its prevalence in modern digital databases. This domain requires a high color
depth of 12 bits per pixel component for accurate analysis by physicians,
primarily in the DICOM format. Standard raster-based compression of images via
filtering is well-known; however, it remains suboptimal in the medical domain
due to non-specialized implementations. This study proposes a lossless medical
image compression algorithm, CompaCT, that aims to target spatial features and
patterns of pixel concentration for dynamically enhanced data processing. The
algorithm employs fractal pixel traversal coupled with a novel approach of
segmentation and meshing between pixel blocks for preprocessing. Furthermore,
delta and entropy coding are applied to this concept for a complete compression
pipeline. The proposal demonstrates that the data compression achieved via
fractal segmentation preprocessing yields enhanced image compression results
while remaining lossless in its reconstruction accuracy. CompaCT is evaluated
in its compression ratios on 3954 high-color CT scans against the efficiency of
industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its
reconstruction performance is assessed with error metrics to verify lossless
image recovery after decompression. The results demonstrate that CompaCT can
compress and losslessly reconstruct medical images, being 37% more
space-efficient than industry-standard compression systems.

该研究提出了一种名为 CompaCT 的无损医学图像压缩算法，通过使用分形分割预处理，以及数据压缩的动态增强，实现了对医学图像的高效压缩和无损重建，其压缩比相比行业标准系统提高了 37%。

CompaCT：适用于无损压缩高色彩 DICOM 医学图像的基于分形的启发式像素分割

CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless  Compression of High-Color DICOM Medical Images

Early works on medical image compression date to the 1980's with the impetus
on deployment of teleradiology systems for high-resolution digital X-ray
detectors. Commercially deployed systems during the period could compress 4,096
x 4,096 sized images at 12 bpp to 2 bpp using lossless arithmetic coding, and
over the years JPEG and JPEG2000 were imbibed reaching upto 0.1 bpp. Inspired
by the reprise of deep learning based compression for natural images over the
last two years, we propose a fully convolutional autoencoder for diagnostically
relevant feature preserving lossy compression. This is followed by leveraging
arithmetic coding for encapsulating high redundancy of features for further
high-density code packing leading to variable bit length. We demonstrate
performance on two different publicly available digital mammography datasets
using peak signal-to-noise ratio (pSNR), structural similarity (SSIM) index and
domain adaptability tests between datasets. At high density compression factors
of >300x (~0.04 bpp), our approach rivals JPEG and JPEG2000 as evaluated
through a Radiologist's visual Turing test.

使用全卷积自编码器结合算术编码实现医学影像的有损压缩，并在公开数据集上进行性能测试，最高压缩比可达 300 倍。