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CLC number: TP391
On-line Access: 2021-09-10
Received: 2020-05-16
Revision Accepted: 2020-10-08
Crosschecked: 2021-08-24
Cited: 0
Clicked: 5395
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.
[1]Clark K, Vendt B, Smith K, et al., 2013. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Dig Imag, 26(6):1045-1057.
[2]Dinh KQ, Lee J, Kim J, et al., 2018. Only-reference video quality assessment for video coding using convolutional neural network. Proc 25th IEEE Int Conf on Image Processing, p.2496-2500.
[3]El-Naqa I, Yang YY, Galatsanos NP, et al., 2004. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imag, 23(10):1233-1244.
[4]Haralick RM, Shanmugam K, Dinstein IH, 1973. Textural features for image classification. IEEE Trans Syst Man Cybern, 3(6):610-621.
[5]Huynh-Thu Q, Ghanbari M, 2008. Scope of validity of PSNR in image/video quality assessment. Electron Lett, 44(13):800-801.
[6]Kamaci N, Altunbasak Y, Mersereau RM, 2005. Frame bit allocation for the H.264/AVC video coder via Cauchy-density-based rate and distortion models. IEEE Trans Circ Syst Video Technol, 15(8):994-1006.
[7]Kwon DK, Shen MY, Kuo CCJ, 2007. Rate control for H.264 video with enhanced rate and distortion models. IEEE Trans Circ Syst Video Technol, 17(5):517-529.
[8]Lazzerini B, Marcelloni F, Vecchio M, 2010. A multi-objective evolutionary approach to image quality/compression trade-off in JPEG baseline algorithm. Appl Soft Comput, 10(2):548-561.
[9]Liu F, Hernandez-Cabronero M, Sanchez V, et al., 2017. The current role of image compression standards in medical imaging. Information, 8(4):131.
[10]Ma S, Gao W, Lu Y, 2005. Rate-distortion analysis for H.264/AVC video coding and its application to rate control. IEEE Trans Circ Syst Video Technol, 15(12):1533-1544.
[11]Ma SW, Si JJ, Wang SS, 2012. A study on the rate distortion modeling for high efficiency video coding. Proc 19th IEEE Int Conf on Image Processing, p.181-184.
[12]Miaou SG, Chen ST, 2004. Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization. IEEE Trans Med Imag, 23(11):1417-1429.
[13]Pan X, Chen ZZ, 2016. Multi-layer quantization control for quality-constrained H.265/HEVC. IEEE Trans Image Process, 26(7):3437-3448.
[14]Patait A, Young E, 2016. High performance video encoding with NVIDIA GPUs. GPU Technology Conf. https://goo.gl/Bdjdgm
[15]Pratt WK, Kane J, Andrews HC, 1969. Hadamard transform image coding. Proc IEEE, 57(1):58-68.
[16]Said A, Pearlman WA, 1996. A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans Circ Syst Video Technol, 6(3):243-250.
[17]Sanchez V, Bartrina-Rapesta J, 2014. Lossless compression of medical images based on HEVC intra coding. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.6622-6626.
[18]Santamaria M, Izquierdo E, Blasi S, et al., 2018. Estimation of rate control parameters for video coding using CNN. IEEE Visual Communications and Image Processing, p.1-4.
[19]Schölkopf B, Smola AJ, Williamson RC, et al., 2000. New support vector algorithms. Neur Comput, 12(5):1207-1245.
[20]Wang HL, Kwong S, 2008. Rate-distortion optimization of rate control for H.264 with adaptive initial quantization parameter determination. IEEE Trans Circ Syst Video Technol, 18(1):140-144.
[21]Wang SJ, Summers RM, 2012. Machine learning and radiology. Med Image Anal, 16(5):933-951.
[22]Wu CY, Su PC, 2013. A content-adaptive distortion-quantization model for H.264/AVC and its applications. IEEE Trans Circ Syst Video Technol, 24(1):113-126.
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