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A software test bed for sharing and evaluating color transfer algorithms for images and 3D objects

Published:30 November 2023Publication History

ABSTRACT

Over the past decades, an overwhelming number of scientific contributions have been published related to the topic of color transfer, where the color statistic of an image is transferred to another image. Recently, this idea was further extended to 3D point clouds. Due to the fact that the results are normally evaluated subjectively, an objective comparison of multiple algorithms turns out to be difficult. Therefore, this paper introduces the ColorTransferLab, a web based test bed that offers a large set of state-of-the-art color transfer implementations. Furthermore, it allows users to integrate their implementations with the ultimate goal of providing a library of state-of-the-art algorithms for the scientific community. This test bed can manipulate both 2D images, 3D point clouds and textured triangle meshes, and it allows us to objectively evaluate and compare color transfer algorithms by providing a large set of objective metrics. As part of ColorTransferLab, we are introducing a comprehensive dataset of freely available images. This dataset comprises a diverse range of content with a wide array of color distributions, sizes, and color depths which helps in appropriately evaluating color transfer. Its comprehensive nature makes it invaluable for accurately evaluating color transfer methods.

References

  1. E. Adelson. 1993. Perceptual organization and the judgment of brightness. Science 262 5142 (1993), 2042–4.Google ScholarGoogle Scholar
  2. M. Afifi, A. Abuolaim, M. Hussien, M. A. Brubaker, and M. S. Brown. 2021a. CAMS: Color-Aware Multi-Style Transfer. arXiv (2021). https://doi.org/10.48550/ARXIV.2106.13920Google ScholarGoogle ScholarCross RefCross Ref
  3. M. Afifi, M. Brubaker, and S. Brown. 2021b. HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.48550/ARXIV.2011.11731Google ScholarGoogle ScholarCross RefCross Ref
  4. B. Berlin and P. Kay. 1969. Basic Color Terms: Their Universal-ity and Evolution. University of California Press, Berkely.Google ScholarGoogle Scholar
  5. X. Cao, W. Wang, K. Nagao, and R. Nakamura. 2020. PSNet: A Style Transfer Network for Point Cloud Stylization on Geometry and Color. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3326–3334. https://doi.org/10.1109/WACV45572.2020.9093513Google ScholarGoogle ScholarCross RefCross Ref
  6. H. Chang, O. Fried, Y. Liu, S. DiVerdi, and A. Finkelstein. 2015. Palette-based Photo Recoloring. ACM Transactions on Graphics (TOG) 34 (07 2015), 139:1–139:11. https://doi.org/10.1145/2766978Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Y. Chang, S. Saito, and M. Nakajima. 2003. A framework for transfer colors based on the basic color categories. Proceedings of the Computer Graphics International Conference (CGI), 176– 181. https://doi.org/10.1109/CGI.2003.1214463Google ScholarGoogle ScholarCross RefCross Ref
  8. R. Charles, H. Su, M. Kaichun, and L. Guibas. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 77–85. https://doi.org/10.1109/CVPR.2017.16Google ScholarGoogle ScholarCross RefCross Ref
  9. G. Chen, C. Yang, and S. Xie. 2006. Gradient-Based Structural Similarity for Image Quality Assessment. In IEEE International Conference on Image Processing (ICIP). 2929–2932. https://doi.org/10.1109/ICIP.2006.313132Google ScholarGoogle ScholarCross RefCross Ref
  10. W. Chen, M. Huang, and C. Wang. 2016. Optimizing color transfer using color similarity measurement. In IEEE/ACIS International Conference on Computer and Information Science (ICIS). 1–6. https://doi.org/10.1109/ICIS.2016.7550799Google ScholarGoogle ScholarCross RefCross Ref
  11. C. Gao, K. Panetta, and S. Agaian. 2015. Color image retrieval and analysis using image color measures. In Mobile Multimedia/Image Processing, Security, and Applications 2015, Sos S. Agaian, Sabah A. Jassim, and Eliza Yingzi Du (Eds.). Vol. 9497. International Society for Optics and Photonics, SPIE, 949702. https://doi.org/10.1117/12.2180605Google ScholarGoogle ScholarCross RefCross Ref
  12. L. Gatys, A. Ecker, and M. Bethge. 2015. A Neural Algorithm of Artistic Style. arXiv (08 2015). https://doi.org/10.1167/16.12.326Google ScholarGoogle ScholarCross RefCross Ref
  13. I. Goudé, R. Cozot, O. Le Meur, and K. Bouatouch. 2021. Example‐Based Colour Transfer for 3D Point Clouds. Computer Graphics Forum (CGF) 40 (08 2021). https://doi.org/10.1111/cgf.14388Google ScholarGoogle ScholarCross RefCross Ref
  14. M. Grogan and R. Dahyot. 2019. L 2 Divergence for Robust Colour Transfer. Computer Vision and Image Understanding (CVIU) 181, C (apr 2019), 39–49. https://doi.org/10.1016/j.cviu.2019.02.002Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. C. Gu, X. Lu, and C. Zhang. 2022. Example-based color transfer with Gaussian mixture modeling. Pattern Recognition 129 (2022), 108716. https://doi.org/10.1016/j.patcog.2022.108716Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. D. Hasler and S. Suesstrunk. 2003. Measuring Colourfulness in Natural Images. Proceedings of SPIE - The International Society for Optical Engineering 5007 (06 2003), 87–95. https://doi.org/10.1117/12.477378Google ScholarGoogle ScholarCross RefCross Ref
  17. Y. Huang, P. Wang, C. Chou, and K. Lin. 2011. An Automatic Selective Color Transfer Algorithm for Images. In Proceedings of the 2011 ACM Symposium on Applied Computing (SAC) (TaiChung, Taiwan) (SAC ’11). Association for Computing Machinery, New York, NY, USA, 66–71. https://doi.org/10.1145/1982185.1982201Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Lee and S. Lee. 2016. Hallucination from Noon to Night Images Using CNN. In SIGGRAPH ASIA 2016 Posters (Macau) (SA ’16). Article 15, 1 pages. https://doi.org/10.1145/3005274.3005320Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Lee, H. Son, G. Lee, J. Lee, S. Cho, and S. Lee. 2020. Deep Color Transfer Using Histogram Analogy. The Visual Computer (Vis. Comput.) 36, 10–12 (oct 2020), 2129–2143. https://doi.org/10.1007/s00371-020-01921-6Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Li and A. Bovik. 2010. Content-partitioned structural similarity index for image quality assessment. Signal Processing: Image Communication 25, 7 (2010), 517–526. https://doi.org/10.1016/j.image.2010.03.004 Special Issue on Image and Video Quality Assessment.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Z. Li, Z. Tan, L. Cao, H. Chen, L. Jiao, and Y. Zhong. 2019. Directive local color transfer based on dynamic look-up table. Signal Processing: Image Communication 79 (2019), 1–12. https://doi.org/10.1016/j.image.2019.06.010Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. F. Luan, S. Paris, E. Shechtman, and K. Bala. 2017. Deep Photo Style Transfer. Conference on Computer Vision and Pattern Recognition (CVPR) (2017). https://doi.org/10.48550/ARXIV.1703.07511Google ScholarGoogle ScholarCross RefCross Ref
  23. A. Mittal, A. Moorthy, and A. Bovik. 2012. No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing (TIP) 21, 12 (2012), 4695–4708. https://doi.org/10.1109/TIP.2012.2214050Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Mittal, R. Soundararajan, and A. Bovik. 2013. Making a “Completely Blind” Image Quality Analyzer. IEEE Signal Processing Letters 20, 3 (2013), 209–212. https://doi.org/10.1109/LSP.2012.2227726Google ScholarGoogle ScholarCross RefCross Ref
  25. S. Nercessian and K. Panetta. 2011. An Image Similarity Measure Using Enhanced Human Visual System Characteristics. Proceedings of SPIE - The International Society for Optical Engineering 8063 (05 2011). https://doi.org/10.1117/12.883301Google ScholarGoogle ScholarCross RefCross Ref
  26. M. Oskarsson. 2021. Robust Image-to-Image Color Transfer Using Optimal Inlier Maximization. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 786–795. https://doi.org/10.1109/CVPRW53098.2021.00088Google ScholarGoogle ScholarCross RefCross Ref
  27. K. Panetta, L. Bao, and S. Agaian. 2016. Novel multi-color transfer algorithms and quality measure. IEEE Transactions on Consumer Electronics (TCE) 62, 3 (2016), 292–300. https://doi.org/10.1109/TCE.2016.7613196Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. K. Panetta, L. Bao, and S. Agaian. 2020. Fast Hue-Division-Based Selective Color Transfer. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) 30, 9 (2020), 2853–2866. https://doi.org/10.1109/TCSVT.2019.2921524Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. F. Pitie and A. Kokaram. 2007. The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer. In European Conference on Visual Media Production (CVMP). 1–9. https://doi.org/10.1049/cp:20070055Google ScholarGoogle ScholarCross RefCross Ref
  30. F. Pitie, A. Kokaram, and R. Dahyot. 2005. N-dimensional probability density function transfer and its application to color transfer. In IEEE International Conference on Computer Vision (ICCV), Vol. 2. 1434–1439 Vol. 2. https://doi.org/10.1109/ICCV.2005.166Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. F. Pitié and R. Dahyot. 2005. Towards automated colour grading. Conference on Visual Media Production (CVMP) (01 2005).Google ScholarGoogle Scholar
  32. F. Pitié, A. Kokaram, and R. Dahyot. 2007. Automated colour grading using colour distribution transfer. Computer Vision and Image Understanding (CVIU) 107, 1 (2007), 123–137. https://doi.org/10.1016/j.cviu.2006.11.011 Special issue on color image processing.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. H. Potechius, T. Sikora, and S. Knorr. 2021. Color Transfer of 3D Point Clouds For XR Applications. In IEEE International Conference on 3D Immersion (IC3D). 1–8. https://doi.org/10.1109/IC3D53758.2021.9687162Google ScholarGoogle ScholarCross RefCross Ref
  34. X. Qian, B. Wang, and L. Han. 2010. An efficient fuzzy clustering-based color transfer method. In International Conference on Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Vol. 2. 520–523. https://doi.org/10.1109/FSKD.2010.5569560Google ScholarGoogle ScholarCross RefCross Ref
  35. Y. Qian, D. Liao, and J. Zhou. 2013. Manifold alignment based color transfer for multiview image stitching. In IEEE International Conference on Image Processing (ICIP). 1341–1345. https://doi.org/10.1109/ICIP.2013.6738276Google ScholarGoogle ScholarCross RefCross Ref
  36. E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. 2001. Color Transfer between Images. IEEE Comput. Graph. Appl. 21, 5 (sep 2001), 34–41. https://doi.org/10.1109/38.946629Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. O. Ronneberger, P. Fischer, and T. Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI)(LNCS, Vol. 9351). Springer, 234–241. http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15aGoogle ScholarGoogle Scholar
  38. D. Ruderman, T. Cronin, and C. Chiao. 1998. Statistics of cone responses to natural images: implications for visual coding. Journal of the Optical Society of America (JOSA) 15, 8 (Aug 1998), 2036–2045. https://doi.org/10.1364/JOSAA.15.002036Google ScholarGoogle ScholarCross RefCross Ref
  39. K. Simonyan and A. Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In International Conference on Learning Representations ICLR. http://arxiv.org/abs/1409.1556Google ScholarGoogle Scholar
  40. H. Talebi and P. Milanfar. 2018. NIMA: Neural Image Assessment. IEEE Transactions on Image Processing (TIP) 27, 8 (aug 2018), 3998–4011. https://doi.org/10.1109/tip.2018.2831899Google ScholarGoogle ScholarCross RefCross Ref
  41. Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing (TIP) 13, 4 (2004), 600–612. https://doi.org/10.1109/TIP.2003.819861Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Z. Wang, E. Simoncelli, and A. Bovik. 2003. Multiscale structural similarity for image quality assessment. In Asilomar Conference on Signals, Systems and Computers (ACSSC), Vol. 2. 1398–1402 Vol.2. https://doi.org/10.1109/ACSSC.2003.1292216Google ScholarGoogle ScholarCross RefCross Ref
  43. T. Welsh, M. Ashikhmin, and K. Mueller. 2002. Transferring Color to Greyscale Images. ACM Transactions on Graphics (ACM Trans. Graph.) 21, 3 (jul 2002), 277–280. https://doi.org/10.1145/566654.566576Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Y. Xiang, B. Zou, and H. Li. 2009. Selective color transfer with multi-source images. Pattern Recognition Letters 30, 7 (2009), 682–689. https://doi.org/10.1016/j.patrec.2009.01.004Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Y. Xiao, L. Wan, C. Leung, Y. Lai, and T. Wong. 2013. Example-Based Color Transfer for Gradient Meshes. IEEE Transactions on Multimedia (TMM) 15, 3 (2013), 549–560. https://doi.org/10.1109/TMM.2012.2233725Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. B. Xie, Y. Li, S. Yang, Y. Xu, and G. Wang. 2023. Combined intrinsic decomposition and histogram matching for color migration. In International Conference on Signal Processing and Communication Technology (SPCT 2022), Vol. 12615. International Society for Optics and Photonics, SPIE, 126150F. https://doi.org/10.1117/12.2673997Google ScholarGoogle ScholarCross RefCross Ref
  47. C. Yao, C. Chang, and S. Chien. 2016. Example-based video color transfer. In IEEE International Conference on Multimedia and Expo (ICME). 1–6. https://doi.org/10.1109/ICME.2016.7552926Google ScholarGoogle ScholarCross RefCross Ref
  48. L. Zhang, Y. Shen, and H. Li. 2014. VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment. IEEE Transactions on Image Processing (TIP) 23, 10 (2014), 4270–4281. https://doi.org/10.1109/TIP.2014.2346028Google ScholarGoogle ScholarCross RefCross Ref
  49. L. Zhang, L. Zhang, X. Mou, and D. Zhang. 2011. FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image Processing (TIP) 20, 8 (2011), 2378–2386. https://doi.org/10.1109/TIP.2011.2109730Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. R. Zhang, P. Isola, A. Efros, E., and O. Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. arxiv:1801.03924Google ScholarGoogle Scholar

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      • Published in

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        CVMP '23: Proceedings of the 20th ACM SIGGRAPH European Conference on Visual Media Production
        November 2023
        112 pages
        ISBN:9798400704260
        DOI:10.1145/3626495

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        • Published: 30 November 2023

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