Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression parameters need to be determined based on the specific quality requirement.In high efficiency video ...Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression 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 vid-eo-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.展开更多
基金the National Natural Science Foundation of China(No.61890954)。
文摘Quality control is of vital importance in compressing three-dimensional(3D)medical imaging data.Optimal com-pression 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 vid-eo-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.
