Quality control is of vital importance in compressing three-dimensional (3D) medical imaging data. Optimal compression parameters need to be determined based on the specific quality requirement. In high efficiency video coding (HEVC), regarded as the state-of-the-art compression tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme in predicting the ideal parameters for 3D medical image compression cannot guarantee satisfactory results. In this paper we propose a learning-based parameter prediction scheme to achieve efficient quality control. Its kernel is a support vector regression (SVR) based learning model that is capable of predicting the optimal QP from both video-based and structural image features extracted directly from raw data, avoiding time-consuming processes such as pre-encoding and iteration, which are often needed in existing techniques. Experimental results on several datasets verify that our approach outperforms current video-based quality control methods.

质量控制对于压缩三维 (3D) 医学成像数据至关重要。需要根据具体的质量要求确定最佳压缩参数。在被视为最先进压缩工具的高效视频编码 (HEVC) 中，量化参数 (QP) 在控制质量方面起着主导作用。直接应用基于视频的方案来预测 3D 医学图像压缩的理想参数并不能保证令人满意的结果。在本文中，我们提出了一种基于学习的参数预测方案，以实现有效的质量控制。它的内核是一个基于支持向量回归 (SVR) 的学习模型，能够从基于视频和直接从原始数据中提取的结构图像特征中预测最佳 QP，避免现有技术中经常需要的诸如预编码和迭代之类的耗时过程。几个数据集的实验结果证实我们的方法优于当前基于视频的质量控制方法。