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[1] Wang Tonghan, Zhang Lu, Jia Huizhen, Kong Youyong, et al. Image quality assessment based on perceptual grouping [J]. Journal of Southeast University (English Edition), 2016, 32 (1): 29-34. [doi:10.3969/j.issn.1003-7985.2016.01.006]
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Image quality assessment based on perceptual grouping()
基于感知分组理论的图像质量评价
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
32
Issue:
2016 1
Page:
29-34
Research Field:
Information and Communication Engineering
Publishing date:
2016-03-20

Info

Title:
Image quality assessment based on perceptual grouping
基于感知分组理论的图像质量评价
Author(s):
Wang Tonghan1;  Zhang Lu2;  Jia Huizhen3;  Kong Youyong1;  Li Baosheng1;  4;  Shu Huazhong1
1Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China
2IETR Lab(UMR CNRS 6164), INSA de Rennes, 20 Avenue des Buttes de Coesmes, CS 70839F-35708 Rennes Cedex 7, France
3School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
4Shandong Cancer Hospital, Jinan 250117, China
王同罕1;  张璐2;  贾惠珍3;  孔佑勇1;  李宝生1;  4;  舒华忠1
1东南大学影像科学与技术实验室, 南京 210096; 2IETR Lab(UMR CNRS 6164), INSA de Rennes, 20 Avenue des Buttes de Coesmes, CS 70839F-35708 Rennes Cedex 7, France; 3南京理工大学计算机科学与工程学院, 南京 210094; 4山东省肿瘤医院, 济南 250117
Keywords:
perceptual grouping;  perceptual image quality assessment;  superpixels;  full reference
感知分组;  感知图像质量评价;  超像素;  全参考
PACS:
TN911.73
DOI:
10.3969/j.issn.1003-7985.2016.01.006
Abstract:
To further explore the human visual system(HVS), the perceptual grouping(PG), which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment(IQA)model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows: first, it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases(LIVE, IVC and MICT)show that the proposed method yields good performance in terms of correlation with human judgments of visual quality.
为了更好地利用人类视觉系统特性, 采用已经被证明在人类视觉系统中具有重要作用的感知分组策略来设计图像质量评价模型.与目前的基于固定窗口的方法不同, 所提方法通过将感知分组策略融入图像质量评价中, 实现了以一种自适应窗口的方式来评价图像质量.算法流程如下:首先, 通过超像素方法将相似像素尽最大限度地聚集到一组;其次, 对参考图像和失真图像的对应超像素进行基于结构相似度的计算;最后, 综合图像中的所有超像素的相似性得到最终的评价结果.在3个图像数据库(LIVE, IVC和MICT)上的实验结果显示, 该方法具有很好的预测性能.

References:

[1] Wang Z, Bovik A C. Modern image quality assessment [M]. San Rafael, CA, USA: Morgan & Claypool, 2006.
[2] Wang Z, Bovik A C, Lu L. Why is image quality assessment so difficult[C]//International Conference on Acoustics, Speech, and Signal Processing. Orlando, FL, USA, 2002: 3313-3316.
[3] Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: From error visibility to structural similarity [J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[4] Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment[C]//37th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA, USA, 2003: 1398-1402.
[5] Li C, Bovik A C. Three-component weighted structural similarity index[C]//SPIE. San Jose, CA, USA, 2009: 72420Q1-72420Q9.
[6] Wang Z, Li Q. Information content weighting for perceptual image quality assessment [J]. IEEE Transactions on Image Processing, 2011, 20(5): 1185-1198. DOI:10.1109/TIP.2010.2092435.
[7] Sheikh H R, Bovik A C. Image information and visual quality [J]. IEEE Transactions on Image Processing, 2006, 15(2): 430-444.
[8] Zhang L, Zhang D, Mou X Q, et al. FSIM: A feature similarity index for image quality assessment [J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386. DOI:10.1109/TIP.2011.2109730.
[9] Zhang L, Shen Y, Li H Y. VSI: a visual saliency-induced index for perceptual image quality assessment [J]. IEEE Transactions on Image Processing, 2014, 23(10): 4270-4281. DOI:10.1109/TIP.2014.2346028.
[10] Xue W F, Zhang L, Mou X Q, et al. Gradient magnitude similarity deviation: a highly efficient perceptual image quality index [J]. IEEE Transactions on Image Processing, 2014, 23(2): 684-695.
[11] Zhang L, Zhang L, Mou X Q, et al. A comprehensive evaluation of full reference image quality assessment algorithms[C]//19th International Conference on Image Processing. Orlando, FL, USA, 2012: 1477-1480.
[12] Lin W S, Jay K C C. Perceptual visual quality metrics: A survey [J]. Journal of Visual Communication and Image Representation, 2011, 22(4): 297-312. DOI:10.1016/j.jvcir.2011.01.005.
[13] Wertheimer M. Laws of organization in perceptual forms(partial translation)[M]. Harcourt Brace Jovanovich, 1938: 71-88.
[14] Achanta R, Shaji A, Smith K, et al. Slicsuperpixels compared to state-of-the-art superpixel methods [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282. DOI:10.1109/TPAMI.2012.120.
[15] Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation [J]. International Journal of Computer Vision, 2004, 59(2):167-181. DOI:10.1023/B:VISI.0000022288.19776.77.
[16] Shi J B, Malik J. Normalized cuts and image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
[17] Moore A P, Prince S J, Warrell J, et al. Superpixel lattices[C]//IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA, 2008: 1-8.
[18] Levinshtein A, Stere A, Kutulakos K N, et al. Turbopixels: Fast superpixels using geometric flows [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2290-2297. DOI:10.1109/TPAMI.2009.96.
[19] Veksler O, Boykov Y, Mehrani P. Superpixels and supervoxels in an energy optimization framework[C]//European Conference on Computer Vision. Heraklion, Greece, 2010: 211-224.
[20] Kong Y Y, Deng Y, Dai Q H. Discriminative clustering and feature selection for brain MRI segmentation [J]. IEEE Signal Processing Letters, 2015, 22(5): 573-577.
[21] Sheikh H R, Wang Z, Cormack L, et al. LIVE image quality assessment database release 2 [EB/OL]. [2007-06-30]. http://live.ece.utexas.edu/.
[22] Horita Y, Shibata K, Kawayoke Y, et al. MICT image quality evaluation database [EB/OL].(2000)[2012-11-12]. http://mict.eng.u-toyama.ac.jp/mictdb.html.
[23] Ninassi A, Calet P L, Autrusseau F. Pseudo no reference image quality metric using perceptual data hiding[C]//SPIE Human Vision and Electronic Imaging. San Jose, CA, USA, 2006: 146-157.
[24] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms [J]. IEEE Transactions on Image Processing, 2006, 15(11): 3440-3451. DOI: 10.1109/TIP.2006.881959.
[25] Chen Y, Huang S Y, Pickwell-MacPherson E. Frequency-wavelet domain deconvolution for terahertz reflection imaging and spectroscopy [J]. Optical Express, 2010, 18(2): 1177-1190. DOI:10.1364/OE.18.001177.
[26] Chen Y, Shi L Y, Feng Q J, et al. Artifact suppressed dictionary learning for low-dose CT image processing [J]. IEEE Transactions on Medical Imaging, 2014, 33(12): 2271-2292. DOI:10.1109/TMI.2014.2336860.

Memo

Memo:
Biographies: Wang Tonghan(1984—), male, graduate; Shu Huazhong(corresponding author), male, doctor, professor, shu.list@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.81272501), the National Basic Research Program of China(973 Program)(No.2011CB707904), Taishan Scholars Program of Shandong Province, China(No. ts20120505).
Citation: Wang Tonghan, Zhang Lu, Jia Huizhen, et al. Image quality assessment based on perceptual grouping[J].Journal of Southeast University(English Edition), 2016, 32(1):29-34. DOI:10.3969/j.issn.1003-7985.2016.01.006.
Last Update: 2016-03-20