Display gamut mapping based on image quality assessment

SONG Chao; WANG Rui-guang; FENG Ying-qiao

doi:10.3788/YJYXS20142906.1050

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Display gamut mapping based on image quality assessment

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- Display gamut mapping based on image quality assessment
- Chinese Journal of Liquid Crystals and Displays Vol. 29, Issue 6, Pages: 1050-1056(2014)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2. 中国科学院大学, 北京 100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20142906.1050
  CLC： TN873
- Received：11 February 2014，
  
  Revised：05 March 2014，
  
  Published：05 December 2014
- 稿件说明：
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宋超, 王瑞光, 冯英翘. 基于图像质量评价的显示器色域映射[J]. 液晶与显示, 2014,29(6): 1050-1056

SONG Chao, WANG Rui-guang, FENG Ying-qiao. Display gamut mapping based on image quality assessment[J]. , 2014,29(6): 1050-1056
宋超, 王瑞光, 冯英翘. 基于图像质量评价的显示器色域映射[J]. 液晶与显示, 2014,29(6): 1050-1056 DOI： 10.3788/YJYXS20142906.1050.

SONG Chao, WANG Rui-guang, FENG Ying-qiao. Display gamut mapping based on image quality assessment[J]. , 2014,29(6): 1050-1056 DOI： 10.3788/YJYXS20142906.1050.

摘要

针对显示器图像颜色复现问题，提出了一种基于图像质量评价和图像分割-融合策略的色域映射算法。详细分析了各类色域映射算法的特点并选取4种算法作为基本色域映射算法。提出了一种色域映射图像质量评价算法，并选用SLIC超像素算法对源图像分割。使用基本色域映射算法对源图像进行映射处理，并计算每个分块内各映射图像与源图像的相似度。根据每个图像分块的相似度，对基本色域映射图像进行选择综合处理并融合成最终图像。最后以LED显示屏和LCD显示器为例，对本文提出的算法和其他基本色域映射算法进行主观评价对比实验。实验结果证明本文算法在图像颜色保真效果上要明显优于其他算法，但是在计算速度上仍有待优化。

Abstract

To solve the image color reproduction problems for display devices

a gamut mapping algorithm based on image quality assessment and image segmentation-fusion strategy was proposed.Firstly

different sorts of gamut mapping algorithms were analyzed in detail and 4 algorithms were chosen as basic algorithms. And then

a gamut mapping image quality assessment method was proposed

and SLIC superpixel algorithm was used to carry out the source image segmentation. Gamut mapping was applied to the source image with the basic algorithms

and similarity was calculated within each image segment between mapped images and source image. According to the calculated similarities

each mapped image was chosen and comprehensively processed

and fused to the final image. Finally

with LED display panel and LCD display as an example

a subjective evaluation experiment was made to the proposed algorithm and other basic gamut mapping algorithms. Experiment result shows that the proposed algorithm is apparently better than other ones on keeping image color fidelity

and yet the calculation speed still needs to be improved.

关键词

Keywords

references

宋超,王瑞光,冯英翘. LED大屏幕显示校正系数配置系统[J]. 液晶与显示, 2013,28(3):392-397. Song C, Wang R G, Feng Y Q. Display calibration coefficient configuration system used in LED panel[J]. Chinese Journal of Liquid Crystals and Displays, 2013, 28(3):392-397.(in Chinese)[2] 宋超,王瑞光,陈宇,等. LED显示屏色域边界的快速计算[J]. 发光学报, 2013, 34(7):924-929. Song C, Wang R G, Chen Y, et al.Fast calculation of color gamut boundary for LED display panel[J].Chinese Journal of Luminescence, 2013, 34(7):924-929.(in Chinese)[3] Morovic J, Luo M R. The fundamentals of gamut mapping: A survey[J].Journal of Imaging Science and Technology, 2001, 45(3): 283-290.[4] Bonnier N, Schmitt F, Brette H, et al.Evaluation of spatial gamut mapping algorithms[C].14th Color and Imaging Conference Final Program and Proceedings,2006: 56-61.[5] Zoliker P, Simon K. Retaining local image information in gamut mapping algorithms[J].IEEE Transactions on Image Processing,2007, 16(3): 664-672.[6] Bala R, de Queiroz R, Eschbach R, et al.Gamut mapping to preserve spatial luminance variations[C].Proceeding of IS&T and SID's 8th Color Imaging Conference: Color Science and Engineering, 2000: 122-126.[7] 王宇庆. 局部方差在图像质量评价中的应用[J]. 中国光学, 2011, 4(5):531-536. Wang Y Q. Application of local variance in image quality assessment[J]. Chinese Optics, 2011, 4(5): 531-536.(in Chinese)[8] Zhang L, Zhang L, Mou X, et al. FSIM: A feature similarity index for image quality assessment[J].IEEE Transactions on Image Processing,2011, 20(8): 2378-2386.[9] Lichtenauer M S, Zoliker P, Lissner I, et al. Learning image similarity measures from choice data[C].Conference on Color in Graphics, Imaging, and Vision, CGIV 2012 Final Program and Proceedings, 2012: 24-30.[10] Achanta R, Shaji A, Smith K, et al. SLIC Superpixels compared to state-of-the-art superpixel methods[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012, 34(11): 2274-2282.[11] He K, Sun J, Tang X.Guided image filtering[C].11th European Conference on Computer Vision,Greece,2010:1-14.

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