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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000234


Learning-based parameter prediction for quality control in 3D medical image compression


Author(s):  Yu-xuan HOU, Zhong REN, Yu-bo TAO, Wei CHEN

Affiliation(s):  State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   3140104190@zju.edu.cn, renzhong@cad.zju.edu.cn

Key Words:  Medical image compression, HEVC, Quality control, Learning-based


Yu-xuan HOU, Zhong REN, Yu-bo TAO, Wei CHEN. Learning-based parameter prediction for quality control in 3D medical image compression[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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%A Wei CHEN
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Abstract: 
quality control is of vital importance in compressing 3D medical imaging data. Optimal compressing parameters need to be determined based on the specific quality requirement. In high efficiency video coding (HEVC), regarded as the state-of-the-art compressing tool, the quantization parameter (QP) plays a dominant role in controlling quality. The direct application of a video-based scheme to predict the ideal parameters for 3D medical image compression cannot guarantee satisfactory results. This paper proposes 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 or iteration, which are often needed in existing techniques. Experimental results on several datasets verify, that our approach outperforms current video-based quality control methods.

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