基于颜色名称的彩色图像质量评价

马畅; 张选德

doi:10.37188/CJLCD.2021-0189

您当前的位置：

首页 >

文章列表页 >

基于颜色名称的彩色图像质量评价

图像处理 | 更新时间：2022-01-19

- 基于颜色名称的彩色图像质量评价
- Color image quality assessment based on colornames
- 液晶与显示 2022年37卷第1期页码：56-65
- 作者机构：
  
  陕西科技大学电子信息与人工智能学院, 陕西西安 710021
- 作者简介：
  
  [ "马畅(1997-), 女, 黑龙江大庆人, 硕士研究生, 2019年于陕西科技大学获得学士学位, 主要从事图像处理及图像质量评价方面的研究。E-mail: machang971@163.com" ]
  [ "张选德(1979-), 男, 宁夏固原人, 博士, 教授, 2013年于西安电子科技大学获得博士学位, 主要从事图像恢复、图像质量评价、视觉跟踪、多光谱图像处理等方面的研究。E-mail: zhangxuande@sust.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61871260)
- DOI：10.37188/CJLCD.2021-0189
  中图分类号： TP391
- 收稿日期：2021-07-16，
  
  修回日期：2021-08-11，
  
  纸质出版日期：2022-01
- 稿件说明：
移动端阅览
马畅, 张选德. 基于颜色名称的彩色图像质量评价[J]. 液晶与显示, 2022,37(1):56-65.

Chang MA, Xuan-de ZHANG. Color image quality assessment based on colornames[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 56-65.
马畅, 张选德. 基于颜色名称的彩色图像质量评价[J]. 液晶与显示, 2022,37(1):56-65. DOI： 10.37188/CJLCD.2021-0189.

Chang MA, Xuan-de ZHANG. Color image quality assessment based on colornames[J]. Chinese journal of liquid crystals and displays, 2022, 37(1): 56-65. DOI： 10.37188/CJLCD.2021-0189.

摘要

图像质量评价（Image Quality Assessment，IQA）是计算机视觉领域研究的基本问题之一。目前，绝大多数图像质量模型都是基于灰度图像构建的，而彩色图像质量评价至今依然是IQA领域的开放性问题。彩色图像质量评价研究的关键在于建立与人类色彩认知能力相一致的色彩信息的量化描述。本文基于颜色名称（Colornames，CN）构建彩色图像质量评价模型，将图像的每个像素值映射为CN概率向量，利用Wasserstein距离计算两幅图像的感知色差，以亮度和梯度特征作为补充，在池化阶段采用显著性加权得到客观图像质量评分。在公开测试数据集上的实验结果表明，提出的模型在TID2008、TID2013和最新的KADID-10k数据集上表现最佳，其SROCC值分别为0.900 9，0.890 1，0.863 7。总体评价效果与目前最好的传统方法（非深度学习方法）相当；而对于颜色失真，则具有明显的优势。

Abstract

Image quality assessment (IQA) is one of the basic research issues in the field of computer vision. At present

most image quality models are constructed based on grayscale images

and color image quality assessment is still an open issue in the field of IQA. The key of color image quality assessment research is to construct a quantitative description of color information consistent with human color cognition. This paper constructs a color image quality assessment model based on colornames(CN). It maps each pixel value of the image to a CN probability vector

uses the Wasserstein distance to calculate the perceived color difference of two images

uses the lightness and gradient features

and uses the saliency weighting in the pooling stage to obtain the objective image quality scores. The experimental results on the public test databases show that the proposed model performs best on TID2008

TID2013 and the latest KADID-10k databases

with SROCC values of 0.900 9

0.890 1 and 0.863 7

respectively. The overall assessment effect is comparable to the current best traditional method (Non-deep learning method). But for color distortion

it has obvious advantages.

关键词

Keywords

references

姚军财 . 基于人眼对比度敏感视觉特性的图像质量评价方法 . 液晶与显示 , 2011 . 26 ( 3 ): 390 - 396 . DOI: 10.3788/YJYXS20112603.0390 http://doi.org/10.3788/YJYXS20112603.0390 .

J YAO . Image quality assessment method based on contrast sensitivity characteristics of human vision system . Chinese Journal of Liquid Crystals and Displays , 2011 . 26 ( 3 ): 390 - 396 . DOI: 10.3788/YJYXS20112603.0390 http://doi.org/10.3788/YJYXS20112603.0390 .

Z WANG , A C BOVIK . Modern Image Quality Assessment , : San Rafael Morgan & Claypool Publishers , 2006 .

Z WANG , A C BOVIK , H R SHEIKH , 等 . Image quality assessment: from error visibility to structural similarity . IEEE Transactions on Image Processing , 2004 . 13 ( 4 ): 600 - 612 . DOI: 10.1109/TIP.2003.819861 http://doi.org/10.1109/TIP.2003.819861 .

W F XUE , L ZHANG , X Q MOU , 等 . Gradient magnitude similarity deviation: a highly efficient perceptual image quality index . IEEE Transactions on Image Processing , 2014 . 23 ( 2 ): 684 - 695 . DOI: 10.1109/TIP.2013.2293423 http://doi.org/10.1109/TIP.2013.2293423 .

L D LI , W H XIA , Y M FANG , 等 . Color image quality assessment based on sparse representation and reconstruction residual . Journal of Visual Communication and Image Representation , 2016 . 38 ( 7 ): 550 - 560 . .

J PREISS , F FERNANDES , P URBAN . Color-image quality assessment: from prediction to optimization . IEEE transactions on image processing , 2014 . 23 ( 3 ): 1366 - 1378 . DOI: 10.1109/TIP.2014.2302684 http://doi.org/10.1109/TIP.2014.2302684 .

D TEMEL , G ALREGIB . Perceptual image quality assessment through spectral analysis of error representations . Signal Processing: Image Communication , 2019 . 70 ( 2 ): 37 - 46 . .

W SUN , Q M LIAO , J H XUE , 等 . SPSIM: a superpixel-based similarity index for full-reference image quality assessment . IEEE Transactions on Image Processing , 2018 . 27 ( 9 ): 4232 - 4244 . DOI: 10.1109/TIP.2018.2837341 http://doi.org/10.1109/TIP.2018.2837341 .

TEMEL D, ALREGIB G. PerSIM: Multi-resolution image quality assessment in the perceptually uniform color domain[C]// 2015 IEEE International Conference on Image Processing , Quebec City, QC, Canada: ICIP, 2015: 1682-1686.

L ZHANG , L ZHANG , X Q MOU , 等 . FSIM: a feature similarity index for image quality assessment . IEEE Transactions on Image Processing , 2011 . 20 ( 8 ): 2378 - 2386 . DOI: 10.1109/TIP.2011.2109730 http://doi.org/10.1109/TIP.2011.2109730 .

KINSMAN T, FAIRCHILD M, PELZ J. Color is not a metric space implications for pattern recognition, machine learning, and computer vision[C]// 2012 Western New York Image Processing Workshop , Rochester, NY, USA: WNYIPW, 2012: 37-40.

J WEIJER , C SCHMID , J VERBEEK , 等 . Learning color names for real-world applications . IEEE Transactions on Image Processing , 2009 . 18 ( 7 ): 1512 - 1523 . DOI: 10.1109/TIP.2009.2019809 http://doi.org/10.1109/TIP.2009.2019809 .

吴金建. 基于人类视觉系统的图像信息感知和图像质量评价[D]. 西安: 西安电子科技大学, 2014.

WU J J. Image information perception and image quality assessment based on human visual system[D]. Xi'an: Xidian University, 2014. (in Chinese)

HOFMANN T. Probabilistic latent semantic indexing[C]// The 22nd annual international ACM SIGIR conference on Research and development . Berkeley, California, USA: SIGIR, 1999: 50-57.

LEVINA E, BICKEL P. The earth mover's distance is the mallows distance: some insights from statistics[C]// Proceedings Eighth IEEE International Conference on Computer Vision , Vancouver, BC, Canada: ICCV, 2001: 251-256.

B JAHNE , H HAUSSECKER , P GEISSLER . Handbook of computer vision and applications , : San Diego Academic press , 1999 .

GEUSEBROEK J M, VANDEN B R, SMEULDERS A W M, et al . Color and scale: the spatial structure of color images[C]// European Conference on Computer Vision , Berlin, Germany: ECCV, 2000: 331-341.

B L VANDEN , J CHRISTIAN . Vision models and applications to image and video processing , : Berlin Springer Science & Business Media , 2013 .

J J WU , G M SHI , W S LIN . Survey of visual just noticeable difference estimation . Frontiers of Computer Science , 2019 . 13 ( 1 ): 4 - 15 . DOI: 10.1007/s11704-016-6213-z http://doi.org/10.1007/s11704-016-6213-z .

ZHANG L, GU Z Y, LI H Y. SDSP: a novel saliency detection method by combining simple priors[C]// 2013 IEEE international conference on image processing . Melbourne, VIC, Australia: ICIP, 2013: 171-175.

N PONOMARENKO , V LUKIN , A ZELENSKY , 等 . TID2008-a database for evaluation of full-reference visual quality assessment metrics . Advances of Modern Radioelectronics , 2009 . 10 ( 4 ): 30 - 45 . .

PONOMARENKO N, IEREMEIEV O, LUKIN V, et al . Color image database TID2013: Peculiarities and preliminary results[C]//E uropean workshop on visual information processing . Paris, France: EUVIP, 2013: 106-111.

E C LARSON , D M CHANDLER . Most apparent distortion: full-reference image quality assessment and the role of strategy . Journal of electronic imaging , 2010 . 19 ( 1 ): 011006 DOI: 10.1117/1.3267105 http://doi.org/10.1117/1.3267105 .

H R SHEIKH , M F SABIR , A C BOVIK . A statistical evaluation of recent full reference image quality assessment algorithms . IEEE Transactions on Image Processing , 2006 . 15 ( 11 ): 3440 - 3451 . DOI: 10.1109/TIP.2006.881959 http://doi.org/10.1109/TIP.2006.881959 .

LIN H H, HOSU V, SAUPE D. KADID-10k: A large-scale artificially distorted IQA database[C]// 2019 Eleventh International Conference on Quality of Multimedia Experience . Berlin, Germany: QoMEX, 2019: 1-3.

WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C]. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers . Pacific Grove, CA, USA: ACSSC, 2003, 2: 1398-1402.

G Y YANG , D S LI , F LU , 等 . RVSIM: a feature similarity method for full-reference image quality assessment . EURASIP Journal on Image and Video Processing , 2018 . ( 1 ): 1 - 15 . .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于快速局部对比度和目标特征的星图弱小目标检测算法

显示设备环境自适应色调映射算法

基于纹理抑制和连续分布估计的显著性目标检测方法

基于人类视觉的快速自动调焦法