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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-08-24
Cited: 0
Clicked: 6463
Citations: Bibtex RefMan EndNote GB/T7714
Learning-based parameter prediction for quality control in three-dimensional medical image compression
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Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen. Learning-based parameter prediction for quality control in three-dimensional medical image compression[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(9): 1169-1178.
@article{title="Learning-based parameter prediction for quality control in three-dimensional medical image compression",
author="Yuxuan Hou, Zhong Ren, Yubo Tao, Wei Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="9",
pages="1169-1178",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000234"
}
%0 Journal Article
%T Learning-based parameter prediction for quality control in three-dimensional medical image compression
%A Yuxuan Hou
%A Zhong Ren
%A Yubo Tao
%A Wei Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 9
%P 1169-1178
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000234
TY - JOUR
T1 - Learning-based parameter prediction for quality control in three-dimensional medical image compression
A1 - Yuxuan Hou
A1 - Zhong Ren
A1 - Yubo Tao
A1 - Wei Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 9
SP - 1169
EP - 1178
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000234
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.
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