色彩复原图像的质量评价方法
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摘要
针对褪色文物数字化保护中色彩复原图像的质量评价问题,研究客观质量评价方法.结合峰值信噪比(PSNR)的计算优势与人眼视觉特征信息熵的结构特性,提出一种基于视觉特征信息熵的彩色图像质量评价方法.该方法建立带权值的质量评价函数和对应评价算法流程,利用归一化方法确定权值.通过评价算法流程计算复原的彩色图像与参考彩色图像的函数值,比较两者的相似程度.值越小,代表相似度越高,对应的复原彩色图像质量越好,以此客观判断色彩复原方法的优劣.实验通过四种性能差异复原方法的质量评价参数比较,表明评价结果与人眼视觉主观感受相一致,验证了所提方法的有效性.
Aiming at the problem of quality evaluation of color restoration image for digital protection of faded cultural relics, the objective quality evaluation methods were researched. Combined the computational advantage of Peak Signal-toNoise Ratio( PSNR) and structure characteristic of human visual feature information entropy, a color image quality evaluation method was proposed based on information entropy of visual features. A quality evaluation function with weights and the corresponding evaluation algorithm process were established, and the weights were determined by normalization method. Then the function value for comparing the similarity between the color restoration image and the reference color image was calculated by using the evaluation algorithm process. The smaller the value was, the higher the similarity was, and the better the corresponding color restoration image quality was, which could be used for the objective judgement of color restoration method.The quality evaluation parameters of four different performance restoration methods were compared. The experimental results show that, the evaluation results are consistent with the subjective perception of human eyes, and the proposed method is effective.
Aiming at the problem of quality evaluation of color restoration image for digital protection of faded cultural relics, the objective quality evaluation methods were researched. Combined the computational advantage of Peak Signal-toNoise Ratio( PSNR) and structure characteristic of human visual feature information entropy, a color image quality evaluation method was proposed based on information entropy of visual features. A quality evaluation function with weights and the corresponding evaluation algorithm process were established, and the weights were determined by normalization method. Then the function value for comparing the similarity between the color restoration image and the reference color image was calculated by using the evaluation algorithm process. The smaller the value was, the higher the similarity was, and the better the corresponding color restoration image quality was, which could be used for the objective judgement of color restoration method.The quality evaluation parameters of four different performance restoration methods were compared. The experimental results show that, the evaluation results are consistent with the subjective perception of human eyes, and the proposed method is effective.
引文
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[2]LIN S,RITCHIE D,FISHER M,et al.Probabilistic color-by-numbers:suggesting pattern colorizations using factor graphs[J].ACM Transactions on Graphics,2013,32(4):Article No.37.
[3]BONNEEL N,SUNKAVALLI K,PARIS S,et al.Example-based video color grading[J].ACM Transactions on Graphics,2013,32(4):96-96.
[4]BOYADZHIEV I,PARIS S,BALA K.User-assisted image compositing for photographic lighting[J].ACM Transactions on Graphics,2013,32(4):Article No.36.
[5]李娜,耿国华,龚星宇,等.采用纹理图像对褪色兵马俑色彩复原方法[J].西安电子科技大学学报(自然科学版),2015,42(4):127-133.(LI N,GENG G H,GONG X Y,et al.The recolor method of fading terracotta warrior using texture image[J].Journal of Xidian University(Natural Science Edition),2015,42(4):127-133.)
[6]仉静,桑庆兵.彩色立体图像质量评价方法[J].计算机应用,2015,35(3):816-820.(ZHANG J,SANG Q B.Quality assessment method of color stereoscopic images[J].Journal of Computer Applications,2015,35(3):816-820.)
[7]李鸿林,张琦,杨大伟.无参考模糊图像质量评价改进算法[J].计算机应用,2014,34(3):797-800.(LI H L,ZHANG Q,YANG D W.Improved algorithm for no-reference quality assessment of blurred image[J].Journal of Computer Applications,2014,34(3):797-800.)
[8]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.
[9]PEI S C,CHEN L H.Image quality assessment using human visual DOG model fused with random forest[J].IEEE Transactions on Image Processing,2015,24(11):3282-3292.
[10]OMARI M,HASSOUNI M E,ABDELOUAHAD A A,et al.A statistical reduced-reference method for color image quality assessment[J].Multimedia Tools and Applications,2015,74(19):8685-8701.
[11]WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error measurement to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[12]SHEIKH H R,BOVIK A C,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.
[13]SHEIKH H R,BOVIK A C.Image information and visual quality[J].IEEE Transactions on Image Processing,2006,15(2):430-444.
[14]谢正祥,王志芳,熊兴良,等.基于视觉感知噪声模型的彩色图像质量评价和彩色图像质量最佳化[J].中国图象图形学报,2010,15(10):1454-1464.(XIE Z X,WANG Z F,XIONG X L,et al.Color image quality assessment based on noise model of human vision perception and color image quality optimization[J].Journal of Image and Graphics,2010,15(10):1454-1464.)
[15]胡义坦,曹杰.基于边缘特征和亮度的彩色图像质量评价[J].电子设计工程,2014,22(1):156-158.(HU Y T,CAO J.Assessment for color image quality based on edge feature and intensity[J].Electronic Design Engineering,2014,22(1):156-158.)
[16]HOJATOLLAH Y,ZHOU W.Objective quality assessment of tone mapped images[J].IEEE Transactions on Image Processing,2013,22(2):657-667.
[17]MOORTHY A K,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.
[18]付伟,顾晓东,汪源源.基于人眼视觉特性的彩色图像质量评价[J].微电子学与计算机,2010,27(2):59-63.(FU W,GU X D,WANG Y Y.Color image quality assessment based on HVS[J].Microelectronics&Computer,2010,27(2):59-63.)
[19]陈强,陈贺新,李文娟.基于3维矩阵变换的彩色图像质量评价方法研究[J].中国图象图形学报,2006,11(11):1732-1735.(CHEN Q,CHEN H X,LI W J.Quality measure of color image based on three dimensional matrix transform[J].Journal of Image and Graphics,2006,11(11):1732-1735.)