基于掩盖效应和梯度信息的无参考噪声图像质量评价改进算法

罗洪艳; 朱子岩; 林睿; 林臻; 廖彦剑

doi:10.11999/JEIT180195

基于掩盖效应和梯度信息的无参考噪声图像质量评价改进算法

doi: 10.11999/JEIT180195

重庆大学生物工程学院重庆 400044

基金项目: 科技部国家重点研发计划(2016YFC0107113)，重庆市重点产业共性关键技术创新专项(CSTC2015ZDCY-ZTZXX0002)

详细信息

作者简介:
罗洪艳：女，1976年生，博士，副教授，研究方向为医学图像处理

朱子岩：男，1993年生，硕士生，研究方向为图像质量评价、数字全息成像

林睿：女，1995年生，硕士生，研究方向为图像质量评价、数字全息成像

林臻：女，1995年生，硕士生，研究方向为图像质量评价、数字全息成像

廖彦剑：男，1976年生，博士，副教授，研究方向为医疗仪器及医学图像处理

通讯作者:
廖彦剑　azurelyj@163.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2018-02-28
- 修回日期: 2018-08-13
- 网络出版日期: 2018-08-21
- 刊出日期: 2019-01-01

Improved No-reference Noisy Image Quality Assessment Based on Masking Effect and Gradient Information

Institute of Bioengineering, Chongqing University, Chongqing 400044, China

Funds: The National Key R & D Program of Ministry of Science and Technology (2016YFC0107113), The Generality Critical Technology Innovation Special Items of Key Industry in Chongqing (CSTC2015ZDCY-ZTZXX0002)

摘要

摘要:
针对目前大多数噪声图像质量评价算法借助域变换或机器学习所带来的运算量大、训练过程繁复等弊端，以及依赖人工设置固定阈值存在普适性不佳的问题，该文改进了一种基于掩盖效应的空域噪声图像质量评价算法。首先依据Hosaka原理提出层递进的分块规则，将图像分成与其内容频率分布高低相符的不同尺寸的子块并赋予相应的掩盖权值；然后通过提取像素点梯度信息，经两步检噪实现子块噪点甄别；再使用掩盖权值对子块噪声污染指标加权得到初步质量评价结果；最终修正和归一化后为整图质量评价结果——改进的无参考峰值信噪比(MNRPSNR)。应用该算法在LIVE和TID2008图像质量评价数据库上对多种噪声类型图像进行实验，结果显示其较目前主流评价算法保有很强竞争力，对传统算法改进效果显著，与人眼主观感受一致性高，普适于多种噪声类型。
- 无参考图像质量评价 /
- 掩盖效应 /
- 噪声检测 /
- 梯度信息
Abstract:
Heavy computational burden, or complex training procedure and poor universality caused by the manual setting of the fixed thresholds are the main issues associated with most of the noise image quality evaluation algorithms using domain transformation or machine learning. As an attempt for solution, an improved spatial noisy image quality evaluation algorithm based on the masking effect is presented. Firstly, according to the layer-layer progressive rule based on Hosaka principle, an image is divided into sub-blocks with different sizes that match the frequency distribution of its content, and a masking weight is assigned to each sub-block correspondingly. Then the noise in the image is detected through the pixel gradient information extraction, via a two-step strategy. Following that, the preliminary evaluation value is obtained by using the masking weights to weight the noise pollution index of all the sub-blocks. Finally, the correction and normalization are carried out to generate the whole image quality evaluation parameter——i.e. Modified No-Reference Peak Signal to Noise Ratio (MNRPSNR). Such an algorithm is tested on LIVE and TID2008 image quality assessment database, covering a variety of noise types. The results indicate that compared with the current mainstream evaluation algorithms, it has strong competitiveness, and also has the significant effects in improving the traditional algorithm. Moreover, the high degree of consistency to the human subjective feelings and the applicability to multiple noise types are well demonstrated.
- No-reference image quality assessment /
- Masking effect /
- Noise detection /
- Gradient information

HTML全文

图 1 改进算法主体框架

下载: 全尺寸图片幻灯片

图 2 改进算法动态阈值与传统算法固定阈值分块结果

下载: 全尺寸图片幻灯片

表 1 数据库信息及实验使用子集

数据库	国家/机构	参考图像数量	主观评价指标	所选失真类型	损伤层级
LIVE	美国/德克萨斯州立大学	29	DMOS	白噪声(WN)	6
TID2008	乌克兰/国家航空航天大学意大利/罗马大学芬兰/坦佩雷理工大学	25	MOS	加性高斯噪声(AGN)	5
				颜色通道加性噪声(ANCC)
				空间相关噪声(SCN)
				掩蔽噪声(MN)
				高频噪声(HFN)
				脉冲噪声(IMN)

下载: 导出CSV

表 2 改进算法与不同检噪阈值下传统NRPSNR算法对monarch图像组评价结果

	DMOS	NRPSNR			MNRPSNR
	DMOS	Nth=10	Nth=50	Nth=100	MNRPSNR
图2(a1)	0.000000	58.35138	69.74312	79.08684	90.0779
图2(a2)	23.94275	50.45518	69.31391	79.51719	77.9929
图2(a3)	28.44905	47.50428	69.01371	79.70202	76.7756
图2(a4)	41.16959	39.03095	49.28878	65.95135	68.3129
图2(a5)	49.08675	36.47912	43.03078	52.72840	65.3847
图2(a6)	65.73029	33.06348	36.52051	41.17893	60.7793

下载: 导出CSV

表 3 MNRPSNR与相关算法特征及在LIVE数据库测试性能指标

算法名称	是否有参考图像	是否需要训练	是否需要域变换	性能指标
算法名称	是否有参考图像	是否需要训练	是否需要域变换	PLCC	SROCC	RMSE
PSNR	是	否	否	0.9050	0.9010	8.4500
SSIM	是	否	否	0.9700	0.9690	3.9540
BIQI	否	是	小波	0.9538	0.9510	8.4094
LBIQ	否	是	小波	0.9761	0.9700	7.9100
DIIVINE	否	是	小波	0.9880	0.9840	4.3100
BLIINDS	否	是	离散余弦	0.9140	0.8900	11.2700
BLIINDS-II	否	是	离散余弦	0.9799	0.9691	N/A
NIQE	否	是	否	0.9773	0.9662	N/A
BRISQUE	否	是	否	0.9851	0.9786	N/A
NRPSNR	否	否	否	0.8681	0.8900	10.9133
MNRPSNR	否	否	否	0.9745	0.9813	4.9369

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表 4 TID2008数据库测试PLCC指标比对

	VSNR	IFC	NQM	UQI	NRPSNR	MNRPSNR
AGN	0.7513	0.6147	0.7397	0.5407	0.6467	0.7922
ANCC	0.7489	0.5628	0.6935	0.4930	0.0402	0.7291
SCN	0.7700	0.6567	0.7757	0.5589	0.1624	0.5808
MN	0.7799	0.7309	0.7575	0.7515	0.7903	0.5164
HFN	0.8861	0.7199	0.9134	0.7059	0.9283	0.9005
IMN	0.6244	0.4950	0.7492	0.4829	0.6403	0.8214

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表 5 TID2008数据库测试SROCC指标比对

	VSNR	IFC	NQM	UQI	NRPSNR	MNRPSNR
AGN	0.7745	0.6204	0.7592	0.5335	0.6276	0.7900
ANCC	0.7725	0.5921	0.7200	0.4798	0.0869	0.7115
SCN	0.7860	0.6403	0.7910	0.5472	0.0491	0.5786
MN	0.7555	0.7374	0.7624	0.7292	0.8018	0.5214
HFN	0.8870	0.7488	0.8952	0.6863	0.9039	0.8852
IMN	0.6460	0.5378	0.7666	0.4951	0.6495	0.8300

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表 6 TID2008数据库测试RMSE指标比对

	VSNR	IFC	NQM	UQI	NRPSNR	MNRPSNR
AGN	0.4005	0.4783	0.4131	0.5112	0.4682	0.3746
ANCC	0.3646	0.4448	0.3942	0.4844	0.5594	0.3832
SCN	0.3878	0.4655	0.3924	0.5055	0.6134	0.5060
MN	0.3745	0.3844	0.3890	0.3955	0.3673	0.5133
HFN	0.4259	0.6069	0.3691	0.6671	0.3563	0.4161
IMN	0.4022	0.4366	0.3402	0.4483	0.3933	0.2948

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表 7 MNRPSNR与相关算法在LIVE数据库上运行时间(s)

算法名称	DIIVINE	BLIINDS-II	NRPSNR	MNRPSNR
平均单幅耗时	149	70	3.45	10.10

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参考文献(31)

HADIZADEH H and VAN BAJIC I. Full-reference objective quality assessment of tone-mapped images[J]. IEEE Transactions on Multimedia, 2018, 20(2): 392–404. doi: 10.1109/TMM.2017.2740023

MA Jian, AN Ping, SHEN Liquan, et al. Reduced-reference stereoscopic image quality assessment using natural scene statistics and structural degradation[J]. IEEE Access, 2018, 6: 2768–2780. doi: 10.1109/ACCESS.2017.2785282

FANG Yuming, YAN Jiebin, LI Leida, et al. No reference quality assessment for screen content images with both local and global feature representation[J]. IEEE Transactions on Image Processing, 2018, 27(4): 1600–1610. doi: 10.1109/TIP.2017.2781307

WANG Zhou, 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. doi: 10.1109/TIP.2003.819861

YE Peng and DAVID D. No-reference image quality assessment based on visual codebook[C]. IEEE International Conference on Image Processing, Brussels, Belgium 2011: 11–14.

WAN Wenfei, WU Jinjian, XIE Xuemei, et al. A novel just noticeable difference model via orientation regularity in DCT domain[J]. IEEE Access, 2017, 5: 22953–22964. doi: 10.1109/ACCESS.2017.2699858

MA Lin, WANG Xu, LIU Qiong, et al. Reorganized DCT-based image representation for reduced reference stereoscopic image quality assessment[J]. Neurocomputing, 2016, 215: 21–31. doi: 10.1016/j.neucom.2015.06.116

SAAD M A, BOVIK A C, and CHARRIER C. A DCT statistics-based blind image quality index[J]. IEEE Signal Processing Letters, 2010, 17(6): 583–586. doi: 10.1109/LSP.2010.2045550

SAAD M A, BOVIK A C, and CHARRIER C. Blind image quality assessment: A natural scene statistics approach in the DCT domain[J]. IEEE Transactions on Image Processing, 2012, 22(8): 3339–3352. doi: 10.1109/TIP.2012.2191563

CHANDLER D M and HEMAMI S S. VSNR: A wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2284–2298. doi: 10.1109/TIP.2007.901820

QIN Ming, LÜ Xiaoxin, CHEN Xiaohui, et al. Hybrid NSS features for no-reference image quality assessment[J]. IET Image Processing, 2017, 11(6): 443–449. doi: 10.1049/iet-ipr.2016.0411

LI Leida, YAN Ya, LU Zhaolin, et al. No-reference quality assessment of deblurred images based on natural scene statistics[J]. IEEE Access, 2017, 5: 2163–2171. doi: 10.1109/ACCESS.2017.2661858

YANG Guangyi, LIAO Yue, ZHANG Qingyi, et al. No-reference quality assessment of noise-distorted images based on frequency mapping[J]. IEEE Access, 2017, 5: 23146–23156. doi: 10.1109/ACCESS.2017.2764126

ABDEL-HAMID L, EI-RAFEI A, and MICHELSON G. No-reference quality index for color retinal images[J]. Computers in Biology and Medicine, 2017, 90: 68–75. doi: 10.1016/j.compbiomed.2017.09.012

MA Kede, LIU Wentao, ZHANG Kai, et al. End-to-end blind image quality assessment using deep neural networks[J]. IEEE Transactions on Image Processing, 2018, 27(3): 1202–1213. doi: 10.1109/TIP.2017.2774045

LIU Tsungjung and LIU Kuanhsien. No-reference image quality assessment by wide-perceptual-domain scorer ensemble method[J]. IEEE Transaction on Image Processing, 2018, 27(3): 1138–1151. doi: 10.1109/TIP.2017.2771422

MITTAL A, MOORTHY A K, and BOVIK A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4659–4708. doi: 10.1109/TIP.2012.2214050

MOORTHY A K and BOVIK A C. Blind image quality assessment: From natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 2011, 20(12): 3350–3364. doi: 10.1109/TIP.2011.2147325

LIU Anmin and LIN Weisi. Image quality assessment based on gradient similarity[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1500–1512. doi: 10.1109/TIP.2011.2175935

王正友, 肖文. 基于掩盖效应的无参考数字图像质量评价[J]. 计算机应用, 2006, 26(12): 2838–2840.

WANG Zhengyou and XIAO Wen. No-reference digital image quality evaluation based on perceptual masking[J]. Computer Applications, 2006, 26(12): 2838–2840.

徐海勇, 郁梅, 骆挺, 等. 基于非负矩阵分解的彩色图像质量评价方法[J]. 电子与信息学报, 2016, 38(3): 578–585. doi: 10.11999/JEIT150610

XU Haiyong, YU Mei, LUO Ting, et al. A color image quality assessment method based on non-negative matrix factorization[J]. Journal of Electronics &Information Technology, 2016, 38(3): 578–585. doi: 10.11999/JEIT150610

蒋平, 张建州. 基于局部最大梯度的无参考图像质量评价[J]. 电子与信息学报, 2015, 37(11): 2587–2593. doi: 10.11999/JEIT141447

JIANG Ping and ZHANG Jianzhou. No-reference image quality assessment based on local maximum gradient[J]. Journal of Electronics &Information Technology, 2015, 37(11): 2587–2593. doi: 10.11999/JEIT141447

SHEIKH H R, WANG Zhou, and CORMACK L. LIVE image quality assessment database, release 2 [EB/OL]. http://live.ece.utexass.edu/research/quality, 2005.

PONOMARENKO N. Tampere image database 2008 TID2008, version 1.0[EB/OL]. http://www.ponomarenko.info/index.html, 2009.

Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, Phase II VQEG [OL]. http://www.vqeg.org/, 2003.

MOORTHY A K and BOVIK A C. A two-step framework for constructing blind image quality indices[J]. IEEE Signal Processing Letters, 2010, 17(5): 513–516. doi: 10.1109/LSP.2010.2043888

TANG Huixuan, JOSHI N, and KAPOOR A. Learning a blind measure of perceptual image quality[C]. IEEE Conference on Computer Vision and Pattern Recognition, Colorado, USA, 2011: 305–312.

MITTAL A, SOUNDARARAJAN R, and BOVIK A C. Making a " completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209–212. doi: 10.1109/LSP.2012.2227726

SHEIKH H R, BOVIK A C, and DE VECIANA G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117–2128. doi: 10.1109/TIP.2005.859389

DAMERA-VENKATA N, KITE T D, GEISLER W S, et al. Image quality assessment based on a degradation model[J]. IEEE Transactions on Image Processing, 2000, 9(4): 636–650. doi: 10.1109/83.841940

WANG Zhou and BOVIK A C. A universal image quality index[J]. IEEE Signal Processing Letters, 2002, 9(3): 81–84. doi: 10.1109/97.995823

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