1.联想集团 神奇工场通讯技术有限公司, 北京 100089
2.北京工商大学 计算机与人工智能学院, 北京 100048
3.北京工商大学 中国轻工业工业互联网与大数据重点实验室, 北京 100048
[ "张欣(1992—),女,山西晋中人,硕士,2018年于北京工商大学获得硕士学位,主要从事图像处理与计算机视觉方面的研究。E-mail:zhangxin191026@ sina.com" ]
[ "乔继红(1972—),女,河北秦皇岛人,博士,副教授,2008年于北京理工大学获得博士学位,主要从事智能检测技术与数据处理方面的研究。E-mail:qiaojh@btbu.edu.cn" ]
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张欣, 乔继红, 张慧妍, 等. 基于颜色空间的彩色图像颜色评价[J]. 液晶与显示, 2023,38(11):1490-1502.
ZHANG Xin, QIAO Ji-hong, ZHANG Hui-yan, et al. Color evaluation of color image based on color space[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(11):1490-1502.
张欣, 乔继红, 张慧妍, 等. 基于颜色空间的彩色图像颜色评价[J]. 液晶与显示, 2023,38(11):1490-1502. DOI: 10.37188/CJLCD.2023-0007.
ZHANG Xin, QIAO Ji-hong, ZHANG Hui-yan, et al. Color evaluation of color image based on color space[J]. Chinese Journal of Liquid Crystals and Displays, 2023,38(11):1490-1502. DOI: 10.37188/CJLCD.2023-0007.
基于手机成像质量颜色评价的必要性,提出一种融合相机主观场景成像色彩和白平衡的自动评测方法(CIQA),以充分提取彩色图像相关特征并模拟人眼视觉感知特性来评价图像颜色。首先使用尺度不变特征变换(Scale-invariant feature transform, SIFT)与透射变换相结合的方法,标识主观图像中ColorChecker标准二十四色卡对应的位置;而后构建离差率最小二乘法模型,并采用专家赋权法和熵权法计算色彩还原和白平衡指标权重分配比例;最后,通过多指标权重值对TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)进行改进,确定各方案与典型正负理想方案的接近程度,实现对智能手机成像质量颜色的优劣排序。对真实场景采集的图片进行实验,并与现有的两种决策方法进行对比验证。结果表明,所提方法能提高评价效率、节省人力,并可以获得与人眼主观判断一致性较好的评价结果。
In order to fully extract relevant features of color images and evaluate image color by simulating visual perception characteristics of human eye, an automatic evaluation method for camera subjective scene imaging color and white balance (CIQA for short) is proposed. Firstly, the corresponding position of ColorChecker standard twenty-four color cards in subjective image is identified, based on the combination of SIFT (Scale-invariant feature transform) and transmission transform,the corresponding position of ColorChecker standard twenty-four color cards in subjective image is identified. Aimed at constructing the deviation least square method model to calculate the weight distribution proportion of color restoration and white balance indicators,the expert grading method and entropy weight method are applied. The proximity between the schemes and positive and negative ideal schemes is calculated by optimized TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method dependent upon multi attribute weights to realize ranking of the smartphones. Experiments are carried out on the pictures collected in real scenes in comparison with the two existing decision-making methods. The results show that the proposed method can improve evaluation efficiency and save manpower, which can obtain evaluation results that are consistent with subjective judgment of human eye.
目标识别指标离差率最小二乘法颜色智能手机
target recognitionindicatordeviation least square methodcolorsmart phone
孟利,刘康康,许小京. 手机相机在现场照相中的应用研究[J]. 中国人民公安大学学报(自然科学版),2018(1):37-42.
MENG L, LIU K K, XU X J. Research on the application of mobile phone camera in field photography[J]. Journal of People's Public Security University of China (Science and Technology), 2018(1): 37-42. (in Chinese)
夏莎莎,张聪,李佳珍,等. 基于手机相机获取冬小麦冠层数字图像的氮素诊断与推荐施肥研究[J]. 中国生态农业学报,2018,26(4):538-546.
XIA S S, ZHANG C, LI J Z, et al. Study on nitrogen nutrition diagnosis and fertilization recommendation of winter wheat using canopy digital images from cellphone camera[J]. Chinese Journal of Eco-Agriculture, 2018, 26(4): 538-546. (in Chinese)
管昉立,徐爱俊. 基于智能手机与机器视觉技术的立木胸径测量方法[J]. 浙江农林大学学报,2018,35(5):892-899. doi: 10.11833/j.issn.2095-0756.2018.05.014http://dx.doi.org/10.11833/j.issn.2095-0756.2018.05.014
GUAN F L, XU A J. Tree DBH measurement method based on smartphone and machine vision technology [J]. Journal of Zhejiang A&F University, 2018, 35(5): 892-899. (in Chinese). doi: 10.11833/j.issn.2095-0756.2018.05.014http://dx.doi.org/10.11833/j.issn.2095-0756.2018.05.014
LISSNER I, PREISS J, URBAN P, et al. Image-difference prediction: from grayscale to color[J]. IEEE Transactions on Image Processing, 2013, 22(2): 435-446. doi: 10.1109/tip.2012.2216279http://dx.doi.org/10.1109/tip.2012.2216279
LEE D, PLATANIOTIS K N. Towards a full-reference quality assessment for color images using directional statistics[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3950-3965. doi: 10.1109/tip.2015.2456419http://dx.doi.org/10.1109/tip.2015.2456419
徐琳,陈强,汪青. 色彩熵在图像质量评价中的应用[J]. 中国图象图形学报,2015,20(12):1583-1592. doi: 10.11834/jig.20151203http://dx.doi.org/10.11834/jig.20151203
XU L, CHEN Q, WANG Q. Application of color entropy to image quality assessment [J]. Journal of Image and Graphics, 2015, 20(12): 1583-1592. (in Chinese). doi: 10.11834/jig.20151203http://dx.doi.org/10.11834/jig.20151203
ZHANG L, ZHANG L, BOVIK A C. A Feature-enriched completely blind image quality evaluator[J]. IEEE Transactions on Image Processing, 2015, 24(8): 2579-2591. doi: 10.1109/tip.2015.2426416http://dx.doi.org/10.1109/tip.2015.2426416
TEMEL D, ALREGIB G. CSV: image quality assessment based on color, structure, and visual system[J]. Signal Processing: Image Communication, 2016, 48: 92-103. doi: 10.1016/j.image.2016.08.008http://dx.doi.org/10.1016/j.image.2016.08.008
LEE D, PLATANIOTIS K N. Toward a no-reference image quality assessment using statistics of perceptual color descriptors[J]. IEEE Transactions on Image Processing, 2016, 25(8): 3875-3889. doi: 10.1109/tip.2016.2579308http://dx.doi.org/10.1109/tip.2016.2579308
LI L D, XIA W H, FANG Y M, et al. Color image quality assessment based on sparse representation and reconstruction residual[J]. Journal of Visual Communication and Image Representation, 2016, 38: 550-560. doi: 10.1016/j.jvcir.2016.04.006http://dx.doi.org/10.1016/j.jvcir.2016.04.006
CAI H, LI L D, YI Z L, et al. Blind quality assessment of gamut-mapped images via local and global statistical analysis[J]. Journal of Visual Communication and Image Representation, 2019, 61: 250-259. doi: 10.1016/j.jvcir.2019.04.006http://dx.doi.org/10.1016/j.jvcir.2019.04.006
PREISS J, FERNANDES F, URBAN P. Color-image quality assessment: from prediction to optimization[J]. IEEE Transactions on Image Processing, 2014, 23(3): 1366-1378. doi: 10.1109/tip.2014.2302684http://dx.doi.org/10.1109/tip.2014.2302684
TEMEL D, ALREGIB G. Perceptual image quality assessment through spectral analysis of error representations[J]. Signal Processing: Image Communication, 2019, 70: 37-46. doi: 10.1016/j.image.2018.09.005http://dx.doi.org/10.1016/j.image.2018.09.005
余伟,徐晶晶,刘玉英,等. 基于自然场景统计的色域映射图像无参考质量评价[J]. 激光与光电子学进展,2020,57(14):141006. doi: 10.3788/lop57.141006http://dx.doi.org/10.3788/lop57.141006
YU W, XU J J, LIU Y Y, et al. No-reference quality evaluation for gamut mapping images based on natural scene statistics[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141006. (in Chinese). doi: 10.3788/lop57.141006http://dx.doi.org/10.3788/lop57.141006
余伟,康凯,袁连海. 基于颜色与结构失真的色域映射图像无参考质量评价算法[J]. 计算机应用研究,2021,38(8):2549-2555.
YU W, KANG K, YUAN L H. No-reference quality evaluation algorithm for gamut mapping images based on color and structural distortions [J]. Application Research of Computers, 2021, 38(8): 2549-2555. (in Chinese)
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: IEEE, 2015: 1682-1686. doi: 10.1109/icip.2015.7351087http://dx.doi.org/10.1109/icip.2015.7351087
ZHANG L, ZHANG L, 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.2011.2109730http://dx.doi.org/10.1109/tip.2011.2109730
GUPTA S, GORE A, KUMAR S, et al. Objective color image quality assessment based on Sobel magnitude[J]. Signal, Image and Video Processing, 2017, 11(1): 123-128. doi: 10.1007/s11760-016-0910-9http://dx.doi.org/10.1007/s11760-016-0910-9
蔡俊,赵超,沈晓波,等. 基于SIFT算法和PyQt的停车位标志识别系统[J]. 湖北大学学报(自然科学版),2022,44(1):1-7.
CAI J, ZHAO C, SHEN X B, et al. Parking space signs recognition system based on SIFT algorithm and PyQt[J]. Journal of Hubei University (Natural Science), 2022, 44(1): 1-7. (in Chinese)
韩彬,杨大利. 基于透射变换的印刷品图像归一化研究[J]. 计算机工程与应用,2019,55(23):188-193, 215.
HAN B, YANG D L. Normalization of printed images based on transmission transformation[J]. Computer Engineering and Applications, 2019, 55(23): 188-193, 215. (in Chinese)
高佳南,吴奉亮,李文福,等. 基于最小二乘法的优化组合权重模型在矿井环境舒适度评价中的应用[J]. 安全与环境工程,2020,27(5):177-183.
GAO J N, WU F L, LI W F, et al. Application of least square method based optimal combined weight model in comfort evaluation of mine environment[J]. Safety and Environmental Engineering, 2020, 27(5): 177-183. (in Chinese)
苟廷佳,陆威文. 基于组合赋权TOPSIS模型的生态文明建设评价——以青海省为例[J]. 统计与决策,2020,36(24):57-60. doi: 10.13546/j.cnki.tjyjc.2020.24.012http://dx.doi.org/10.13546/j.cnki.tjyjc.2020.24.012
GOU T J, LU W W. Evaluation of ecological civilization construction based on combination weighted TOPSIS model-taking Qinghai province as an example[J]. Statistics & Decision, 2020, 36(24): 57-60. (in Chinese). doi: 10.13546/j.cnki.tjyjc.2020.24.012http://dx.doi.org/10.13546/j.cnki.tjyjc.2020.24.012
郜锦茹. 智能手机成像模组的质量评价方法[D]. 杭州:杭州电子科技大学,2018.
GAO J R. Quality evaluation methods of smart phone imaging module[D]. Hangzhou: Hangzhou Dianzi University, 2018. (in Chinese)
LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. doi: 10.1023/b:visi.0000029664.99615.94http://dx.doi.org/10.1023/b:visi.0000029664.99615.94
安玮,李宏,徐晖,等. 模式识别中的透射变换与仿射变换[J]. 系统工程与电子技术,1999,21(1):55-60. doi: 10.3321/j.issn:1001-506X.1999.01.014http://dx.doi.org/10.3321/j.issn:1001-506X.1999.01.014
AN W, LI H, XU H, et al. Projective transformation and affine transformation in pattern recognition[J]. Systems Engineering and Electronics, 1999, 21(1): 55-60. (in Chinese). doi: 10.3321/j.issn:1001-506X.1999.01.014http://dx.doi.org/10.3321/j.issn:1001-506X.1999.01.014
张小燕,程韦. 快速色差评价[J]. 湖南工业大学学报,2011,25(2):77-80. doi: 10.3969/j.issn.1673-9833.2011.02.019http://dx.doi.org/10.3969/j.issn.1673-9833.2011.02.019
ZHANG X Y, CHENG W. Fast color difference evaluation[J]. Journal of Hunan University of Technology, 2011, 25(2): 77-80. (in Chinese). doi: 10.3969/j.issn.1673-9833.2011.02.019http://dx.doi.org/10.3969/j.issn.1673-9833.2011.02.019
顿雄,付强,李浩天,等. 计算成像前沿进展[J]. 中国图象图形学报,2022,27(6):1840-1876. doi: 10.11834/jig.220061http://dx.doi.org/10.11834/jig.220061
DUN X, FU Q, LI H T, et al. Recent progress in computational imaging[J]. Journal of Image and Graphics, 2022, 27(6): 1840-1876. (in Chinese). doi: 10.11834/jig.220061http://dx.doi.org/10.11834/jig.220061
赵磊,张文,孙振国,等. 基于色彩分割及信息熵加权特征匹配的刹车片图像分类算法[J]. 清华大学学报(自然科学版),2018,58(6):547-552. doi: 10.16511/j.cnki.qhdxxb.2018.26.025http://dx.doi.org/10.16511/j.cnki.qhdxxb.2018.26.025
ZHAO L, ZHANG W, SUN Z G, et al. Brake pad image classification algorithm based on color segmentation and information entropy weighted feature matching[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(6): 547-552. (in Chinese). doi: 10.16511/j.cnki.qhdxxb.2018.26.025http://dx.doi.org/10.16511/j.cnki.qhdxxb.2018.26.025
SAATY T L. Modeling unstructured decision problems-the theory of analytical hierarchies[J]. Mathematics and Computers in Simulation, 1978, 20(3): 147-158. doi: 10.1016/0378-4754(78)90064-2http://dx.doi.org/10.1016/0378-4754(78)90064-2
张俊升,徐晶晶,余伟. 面部美化图像质量无参考评价方法[J]. 计算机应用,2020,40(4):1184-1190.
ZHANG J S L, XU J J, YU W. No-reference image quality assessment method for facial beautification image[J]. Journal of Computer Applications, 2020, 40(4): 1184-1190. (in Chinese)
PONOMARENKO N, JIN L N, IEREMEIEV O, et al. Image database TID2013: peculiarities, results and perspectives[J]. Signal Processing: Image Communication, 2015, 30: 57-77. doi: 10.1016/j.image.2014.10.009http://dx.doi.org/10.1016/j.image.2014.10.009
YANG Y, MING J, YU N H. Color image quality assessment based on CIEDE2000[J]. Advances in Multimedia, 2012, 2012: 273723. doi: 10.1155/2012/273723http://dx.doi.org/10.1155/2012/273723
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