Abstract
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.
摘要
质量控制是三维医学图像压缩过程至关重要的环节, 需设定最佳图像压缩参数才能满足特定的压缩质量需求. 高效视频编码 (HEVC) 是目前最先进的压缩工具. 其中, 量化参数 (QP) 对HEVC的压缩质量控制起决定性作用, 如能对其精确预测, 就能完成质量控制的目标; 然而, 直接将视频压缩领域中的预测方法套用到三维医学数据压缩, 精度和效率无法取得令人满意的结果. 为此, 提出一种基于学习的参数预测方法, 用于实现三维医学图像压缩中的高效质量控制. 本文方法基于支撑向量回归 (SVR), 可以直接利用从原始数据中提取的基于视频的特征与基于结构的特征来预测最佳QP, 无需经过耗时长的预编码或迭代过程. 在若干数据集上的实验结果证明, 本文方法比现有方法在预测准确度和速度上表现更好.
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Zhong REN and Wei CHEN designed the research. Yuxuan HOU implemented the algorithm and drafted the paper. Yubo TAO participated in the technical discussions. Zhong REN, Yubo TAO, and Wei CHEN revised and finalized the paper.
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Yuxuan HOU, Zhong REN, Yubo TAO, and Wei CHEN declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (No. 61890954)
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Hou, Y., Ren, Z., Tao, Y. et al. Learning-based parameter prediction for quality control in three-dimensional medical image compression. Front Inform Technol Electron Eng 22, 1169–1178 (2021). https://doi.org/10.1631/FITEE.2000234
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DOI: https://doi.org/10.1631/FITEE.2000234