Abstract
The atmospheric perspective effect is a physical phenomenon relating to the effect that atmosphere has on distant objects, causing them to be lighter and less distinct. The exaggeration of this effect by artists in 2D images increases the illusion of depth, thereby making the image more appealing. This chapter addresses the enhancement of the atmospheric perspective effect in landscape photographs, by the manipulation of depth-aware lightness and saturation contrast values. The form of this manipulation follows the organisation of such contrast in landscape paintings. The rational behind this manipulation is based on a statistical study which has shown clearly that the saturation contrast and lightness contrast between and within the depth planes in paintings are more purposefully organised than those in photographs. This contrast organisation in paintings respects the existing contrast relationships within a natural scene governed by the atmospheric perspective effect, yet also exaggerates upon them. In our approach, the depth-aware lightness and saturation contrast revealed in landscape paintings guides the mapping of contrasts in photographs. This contrast mapping is formulated as an optimisation problem that simultaneously considers the desired inter-contrast, intra-contrast, and some gradient constraints. Experimental results demonstrate that by using this proposed method, both the visual appeal and the illusion of depth in the photographs are effectively improved.
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References
Arbelaez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic segmentation using regions and parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3378–3385 (2012)
Bae, S., Paris, S., Durand, F.: Two-scale tone management for photographic look. ACM Trans. Graph. 25(3), 637–645 (2006)
Bailey, R.: Perception-guided image manipulation. Ph.D. Dissertation, Department of Computer Science and Engineering, Washington University in St. Louis (2007)
Beaudot, W., Mullen, K.: How long range is contour integration in human color vision? Vis. Neurosci. 15, 51–64 (2003)
Bhat, P., Zitnick, L., Cohen, M., Curless, B.: Gradientshop: a gradient-domain optimization framework for image and video filtering. ACM Trans. Graph. 29(2), 10:1–10:15 (2010)
Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. Proceedings of the IEEE International Conference on Computer Vision 1, 105–112 (2001)
Chen, S., Beghdadi, A.: Natural enhancement of color image. EURASIP J. Image Video Process. 2010, 2:1–2:30 (2010)
Csurka, G., Larlus, D., Perronnin, F.: What is a good evaluation measure for semantic segmentation? In: Proceedings of the British Machine Vision Conference, pp. 1–11 (2013)
Dale, K., Johnson, M.K., Sunkavalli, K., Matusik, W., Pfister, H.: Image restoration using online photo collections. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2217–2224 (2009)
Datta, R., Wang, J.: Acquine: aesthetic quality inference engine - real-time automatic rating of photo aesthetics. In: Proceedings of the ACM Multimedia Information Retrieval, pp. 421–424 (2010)
Dunning, W.V.: Changing Images of Pictorial Space: A History of Spatial Illusion in Painting. Syracuse University Press, Syracuse (1991)
Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010)
Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002)
Hasler, D., Susstrunk, S.: Measuring colourfulness in natural images. In: IS&T/SPIE Electronic Imaging 2003: Human Vision and Electronic Imaging VIII, vol. 5007, pp. 87–95 (2003)
Huang, H., Xiao, X.Z.: Example-based contrast enhancement by gradient mapping. Visual Comput. 26, 731–738 (2010)
Johnson, M.K., Dale, K., Avidan, S., Pfister, H., Freeman, W.T., Matusik, W.: Cg2real: improving the realism of computer generated images using a large collection of photographs. IEEE Trans. Visual Comput. Graph. 17, 1273–1285 (2011)
Jung, J.I., Lee, J.H., Shin, I.Y., Moon, J.H., Ho, Y.S.: Improved depth perception of single-view images. ECTI Trans. Electr. Eng. Electron. Commun. 8(2), 164–172 (2010)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2169–2178 (2006)
Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. Graph. 25, 646–653 (2006)
Luft, T., Colditz, C., Deussen, O.: Image enhancement by unsharp masking the depth buffer. ACM Trans. Graph. 25, 1206–1213 (2006)
Majumder, A., Irani, S.: Perception-based contrast enhancement of images. ACM Trans. Appl. Percept. 4(3), 1–22 (2007)
Mattingly, D.B.: The Digital Matte Painting Handbook. Wiley Publishing Inc, New York (2011)
Narasimhan, S.G.: Models and Algorithms for Vision through the Atmosphere. Columbia University, Ph.D. thesis (2003)
Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vision 48, 233–254 (2002)
O’Shea, R.P., Blackburn, S.G., Ono, H.: Contrast as a depth cue. Vision. Res. 34(12), 1595–1604 (1994)
Rigau, J., Feixas, M., Sbert, M.: Informational aesthetics measures. IEEE Comput. Graph. Appl. 28(2), 24–34 (2008)
Saxena, A., Sun, M., Ng, A.Y.: Make3d: learning 3d scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2009)
Sen, D., Pal, S.K.: Automatic exact histogram specification for contrast enhancement and visual system based quantitative evaluation. IEEE Trans. Image Process. 20(5), 1211–1220 (2011)
Sheppard, R.: Landscape Photography: From Snapshots to Great Shots. Peachpit Press, San Francisco (2012)
Sievers, A.H.: Master Drawings from Smith College Museum of Art. Hudson Hills Press, London (2000)
Wang, B., Yu, Y., Xu, Y.Q.: Example-based image color and tone style enhancement. ACM Trans. Graph. 30(4), 64:1–64:12 (2011)
Yendrikhovskij, S.N., Blommaert, F.J.J., de Ridder, H.: Perceptually optimal color reproduction. In: Proceedings of the 6th Color Imaging Conference: Color Science, Systems, and Applications, vol. 3299, pp. 274–281 (1998)
Zhang, X., Constable, M., Chan, K.L.: Aesthetic enhancement of landscape photographs as informed by paintings across depth layers. In: Proceedings of the IEEE International Conference on Image Processing, pp. 1137–1140 (2011)
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Zhang, X., Constable, M., Chan, K.L., Yu, J., Junyan, W. (2018). Atmospheric Perspective Effect Transfer for Landscape Photographs. In: Computational Approaches in the Transfer of Aesthetic Values from Paintings to Photographs. Springer, Singapore. https://doi.org/10.1007/978-981-10-3561-6_6
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