Assessing the Quality of Tone Mapped Images Based On Structural Similarity
Keywords:
Dynamic Range, Tone Mapping, Quality Assessment, Structural SimilarityAbstract
with recent advances in imaging and computer graphics technologies, HDR images are becoming more widely available. To display high dynamic range (HDR) images onto conventional displayable devices that have low dynamic range (LDR) such as monitors and printers, an increasing number of tone mapping operators (TMOs) that convert HDR to LDR images have been developed. In order to assess the quality of several TMO, an objective quality assessment algorithm named Tone Mapped image Quality Index (TMQI) is proposed for tone mapped images. Initially the HDR image is generated for which the three low, Mid and high exposure images are subjected to HDR image generation and the created HDR image is stored in .hdr format which serves as the input to Tone mapped Images Quality Index Algorithm (TMQI). It combines a multi-scale signal fidelity measure based on a modified structural similarity (SSIM) index and a naturalness measure based on intensity statistics of natural images. It converts high dynamic range (HDR) to low dynamic range (LDR) images and also generate the multi scale structural fidelity measure. Then the laplacian pyramid is computed for Exposure images. Finally the structural fidelity and laplacian pyramid images are fused which produces the tone mapped images.
References
E. Reinhard, G. Ward, S. Pattanaik, P. Debevec, W. Heidrich, and K. Myszkowski, “High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting” San Mateo, CA: Morgan Kaufmann, page no(187-219),2010.
E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” in Proc. 29th Annu. Conf. Comput. Graph. Interact. Tech., vol. 21. 2002, pp. 267–276.
G. W. Larson, H. Rushmeier, and C. Piatko, “A visibility matching tone reproduction operator for high dynamic range scenes,” IEEE Transaction Visual Computer. Graphics, vol. 3, no. 4, page no 291–306, Oct.–Dec. 1997.
F. Drago, K. Myszkowski, T. Annen, and N. Chiba, “Adaptive logarithmic mapping for displaying high contrast scenes,” Comput. Graph. Forum, vol. 22, no. 3, pp. 419–426, 2003.
R. Fattal, D. Lischinski, and M. Werman, “Gradient domain high dynamic range compression,” in Proceedings 29th Annual Conference Computer Graphics Interaction Technology, page no: 249–256, 2002.
Huei-Yung Lin, Xin-Han Chou “An Image Quality Assessment Technique using Defocused Blur as Evaluation Metric” in International Computer Science and Engineering VISAPP (1) 2013: 101-104.
A. J. Kuang, H. Yamaguchi, G. M. Johnson, and M. D. Fairchild, “Testing HDR image rendering algorithms,” in Proceedings IST/SID Color Image. Conference on 2004, page no: 315–320.
P. Ledda, A. Chalmers, T. Troscianko, and H. Seetzen, “Evaluation of tone mapping operators using a high dynamic range display,” ACM Transaction Graphics vol. 24, no. 3, page no. 640–648, 2005.
A. Yoshida, V. Blanz, K. Myszkowski, and H. Seidel, “Perceptual evaluation of tone mapping operators with real-world scenes,” Proceedings SPIE, Human Visual Electron. Image, vol. 5666, page no:192–203, Jan 2005.
M. ˇ Cadík, M. Wimmer, L. Neumann, and A. Artusi, “Image attributes and quality for evaluation of tone mapping operators,” in Proceedings 14th Pacific Conference Computer Graphic, page no. 35–44, April 2006.
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