Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

DING Yong; LI Nan

doi:10.11999/JEIT151430

Volume 38 Issue 9

Sep. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(9): 2365-2370

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

Citation:

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

Citation:

DING Yong, LI Nan. Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2365-2370. doi: 10.11999/JEIT151430

PDF( 802 KB)

Image Quality Assessment Based on Non-localHigh Dimensional Feature Analysis

doi: 10.11999/JEIT151430

DING Yong¹,
LI Nan¹

Funds:

Items: The National 863 Program of China (2015AA016704c), Zhejiang Provincial Natural Science Foundation (LY14F020028)

Received Date: 2015-12-17
Rev Recd Date: 2016-04-19
Publish Date: 2016-09-19

Abstract

Abstract

Traditionally, low dimensional features for partial information are extracted to analyze image quality. Though high dimensional features are difficult to be analyzed, they contain more information to fully analyze image quality. On this condition, this paper proposes an image quality assessment method based on non-local high dimensional feature analysis after optimized data sampling. Firstly, image data is filtered by using block matching method and dimensionally reduced by Principal Component Analysis (PCA). Secondly, Kernel Independent Component Analysis (KICA) is applied to extract high dimensional features. The features are finally synthesized to evaluate image quality based on natural image statistics. The experimental results show that the proposed method keeps accordance with human objective perception.
- Image quality assessment,
- High dimensional feature,
- Non-local,
- Kernel Independent Component Analysis (KICA)

FullText(HTML)

References(14)

References

HE L, GAO F, HOU W, et al. Objective image quality assessment: A survey[J]. International Journal of Computer Mathematics, 2014, 91(11): 2374-2388. doi: 10.1080/ 00207160.2013.816415.

WANG Zand BOVIK A C. Reduced-and no-reference image quality assessment[J]. IEEE Signal Processing Magazine, 2011, 28(6): 29-40. doi: 10.1109/MSP.2011.942471.

HU A, ZHANG R, YIN D, et al. Image quality assessment using a SVD-based structural projection[J]. Signal Processing: Image Communication, 2014, 29(3): 293-302. doi: 10.1016/ j.image.2014.01.007.

HYVARINEN A, HURRI J, and HOYER P O. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision[M]. US, Springer Science Business Media, 2009, Chap 4. doi: 10.1007/978-1-84882-491-1.

HORE A and ZIOU D. Image quality metrics: PSNR vs. SSIM[C]. 2010 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, 2010: 2366-2369. doi: 10.1109/ICPR.2010.579.

GAO X, LU W, LI X, et al. Wavelet-based contourlet in quality evaluation of digital images[J]. Neurocomputing, 2008, 72(1): 378-385. doi: 10.1016/j.neucom.2007.12.031.

DING Y, DAI H, and WANG S. Image quality assessment scheme with topographic independent components analysis for sparse feature extraction[J]. Electronics Letters, 2014, 50(7): 509-510. doi: 10.1049/el.2013.4298.

LUO C, WANG Y, DING Y, et al. Image quality assessment based on independent component analysis[C]. IEEE 2014 12th International Conference on. Signal Processing (ICSP), Hangzhou, China, 2014: 922-927. doi: 10.1109/ICOSP.2014. 7015139.

MANIKANDAN L C and SELVAKUMAR R K. A new survey on block matching algorithms in video coding[J]. International Journal of Engineering Research, 2014, 3(2): 121-125.

ZHONG H, MA K, and ZHOU Y. Modified BM3D algorithm for image denoising using nonlocal centralization prior[J]. Signal Processing, 2015, 106: 342-347. doi: 10.1016/ j.sigpro.2014.08.014.

SHLENS J. A tutorial on principal component analysis[J]. Eprint Arxiv, 2014, 58(3): 219-226.

ABDI H and WILLIAMS L J. Principal component analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459. doi: 10.1002/wics.101.

BACH F R and JORSAN M I. Kernel independent component analysis[J]. The Journal of Machine Learning Research, 2003, 3: 1-48.

LIU M and YANG X. Image quality assessment using contourlet transform[J]. Optical Engineering, 2009, 48(10): 107201-10.

Relative Articles

Supplements(0)

Cited By

Proportional views