发表机构
Faculty of Computing, Harbin Institute of Technology; School of Electronics Engineering and Computer Science, Peking University; Pengcheng Laboratory; Huawei Tech. Company, Ltd.（哈尔滨工业大学计算学部; 北京大学电子工程与计算机科学学院; 鹏城实验室; 华为技术有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对体医学图像无损压缩的效率与部署难题，提出无需DNN和外部数据的三平面上下文树方法，在多数据集上达到与DNN方法相当的压缩性能，且计算成本低、编码速度快，实用性强。
AI 中文摘要
体医学图像的无损压缩对于数据保真度至关重要的临床和研究应用而言极为重要。传统压缩方法因采用刚性手工模型，效率常受限制；而基于深度神经网络（DNN）的压缩方法虽有效，但需大量计算资源，阻碍了其在资源受限场景中的部署。为应对这些挑战，我们提出一种新颖的基于三平面上下文树（TCT）的无损体医学图像压缩方法，该方法无需依赖DNN或外部训练数据即可实现高性能。为利用片内和片间冗余，我们引入紧凑的三平面上下文表示，将复杂的3D上下文建模分解为三个正交平面上的高效2D建模。通过将该表示与上下文树框架结合，我们开发了一种采用自适应二叉树结构的输入特定TCT模型。在每个树节点处，模型从一组基于三平面的预测器和上下文特征提取器中动态选择，实现针对局部结构特征量身定制的数据自适应上下文建模。我们不采用离线训练，而是采样输入体积的一个子集，通过迭代构建和剪枝优化最小描述长度（MDL）来学习TCT模型。利用学习到的TCT模型，每个像素检索其对应的上下文，使用上下文指定的预测器计算预测残差，并基于相关直方图进行熵编码。实验结果表明，所提方法在多个数据集上实现了与近期基于DNN的方法相当的压缩性能，同时保持了低计算成本和快速编码速度，使其在实际应用中极具适用性。
英文摘要
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to rigid, handcrafted models. Conversely, deep neural network (DNN)-based compression methods, while effective, demand substantial computational resources, hindering deployment in resource-constrained settings. To address these challenges, we propose a novel tri-plane context tree (TCT)-based method for lossless volumetric medical image compression that delivers high performance without relying on DNNs or external training data. To exploit intra-slice and inter-slice redundancies, we introduce a compact tri-plane context representation that decomposes complex 3D context modeling into efficient 2D modeling on three orthogonal planes. By integrating this representation with a context tree framework, we develop an input-specific TCT model employing an adaptive binary tree structure. At each tree node, the model dynamically selects from a suite of tri-plane based predictors and contextual feature extractors, enabling data-adaptive context modeling tailored to local structural characteristics. Instead of offline training, we sample a subset of the input volume to learn the TCT model by optimizing the minimum description length (MDL) through iterative construction and pruning. With the learned TCT model, each pixel retrieves its corresponding context, computes the prediction residual using the predictor dictated by the context, and performs entropy encoding based on the associated histograms. Experimental results demonstrate that the proposed method achieves compression performance on par with recent DNN-based methods on multiple datasets, while maintaining low computational cost and fast coding speeds, making it highly applicable in practice.
