□著顯示器技術的發展，比傳統顯示器還具有更高動態範圍的顯示設備已稍具雛型，同時更多高動態範圍的影片及影像也因為相關技術的成熟而逐漸容易取得。然而，現有的影像和影片大多還是在低動態範圍的數位格式中保存，因此如何善用反色調對映運算 (inverse tone mapping operator)擴展亮度使之能夠得到接近於真實世界的動態範圍是近來廣被研究的課題。
現存有許多反色調對映運算子，但這些運算子的特性和表現還有需要被仔細評估和分析的空間。傳統評估反色調對映運算子的方法需要一個目標影像來當作評估的標準，然而取得相近於真實世界動態範圍的影像是相當困難且並不總是能達成的事情。前人研究提出了對於一對不同動態範圍的影像，都能夠對比度增加或減少的變化情況進行評估的影像品質評量方式，然而此方法較為複雜。
本篇論文利用臨界可視失真值(Just Notice Difference)的概念提出了較簡略的方式來評斷影像，利用這個方式來評量反色調對映運算子從原始低動態範圍到後來高動態範圍的對比變化情形，因此歸納出這些不同反色調對映運算子的表現及特性，並且能夠分析不同的色調對映運算子參數所產生的影響。此外本論文亦提出了結合多種評估影像特徵如亮度、對比以及色彩豐富程度的無參考影像品質評量方式，而評量的結果也符合前述以臨界可視失真值為基礎的分析結果。綜合以上兩種方式，我們能夠在沒有目標影像的情況下評估與分析反色調對映運算子。

Next-generation display technologies provide significantly improved dynamic range over conventional display devices. In the long run, advanced CCD or CMOS sensor technologies and data formats will provide high dynamic range (HDR) content for these display devices. Despite the increasing availability of HDR content, legacy low dynamic range (LDR) images and videos represent the majority of content in the near term. Recently more and more researches work on reproducing real-world appearance images through LDR images using inverse tone mapping methods. Therefore, evaluation metrics to qualify the performance of inverse tone mapping operators (iTMO) are needed to understand the effects of important features such as nonlinearity. Most evaluation requires a reference ground truth to compare with the generated HDR images. However, a reference HDR image may not be available or hard to verify as a ground truth. Dynamic range independent quality assessment metric is proposed to measure visual distortion based on the detection and classification of visible change on an image pair. However, complex contrast detection predictor and careful calibration are needed.
In this work, we propose a new quality assessment metric to detect the visual distortion based on the probability of various contrast change using JND model on the iTMO curves directly. We also apply our quality assessment scheme on image structures by comparing HDR images generated by various iTMO with the original LDR images. Various contrast changes include loss of original visible contrast, enhancement of original invisible contrast, and inverse of original contrast. Results of our metric using the iTMO and image structure matches with that of the dynamic range independent assessment method. Our metric does not require complex calibration and the computation is simple. This method also help to understand the effect of various parameters on the iTMO curves and analyze which method is suitable for a given image.
In addition, we also propose a blind image quality assessment that measures test images without information from reference images. The blind image estimation method contains attributes of contrast, brightness, and colorfulness. The analysis results of contrast and brightness matches with that of the JND-based contrast analysis and the characteristics of iTMO curves. Compared with previous approaches, our evaluation metric provides valuable quality assessment results consistent with human perception and the computation complexity is much lower.

[1] H. Seetzen, W. Heidrich, W. Stuerzlinger, G. Ward, L. Whitehead, M. Trentacoste, A. Ghosh and A. Vorozcovs, “High Dynamic Range Display Systems,” ACM Trans. Grap,. vol. 23, no. 3, pp.760–768, 2004.
[2] S. Mann, R. W. Picard, S. Mann and R. P. Being, “Undigital’ with Digital Cameras: Extending Dynamic Range by Combining Differently Exposed Pictures,” Proc. of IST’s 48th Annual Conference, pp. 442–448, May 1995.
[3] P. E. Debevec and J. Malik, “Recovering High Dynamic Range Radiance Maps from Photographs,” In SIGGRAPH 97, pp.369-378, Aug. 1997.
[4] S. K. Nayar and T. Mitsunaga, “High Dynamic Range Imaging: Spatially Varying Pixel Exposures,” In Proc. CVPR, pp.473-479, 2000.
[5] A. G. Rempel, M. Trentacoste, H. Seetzen and H. D. Young, “Ldr2Hdr: On-the-fly Reverse Tone Mapping of Legacy Video and Photographs,” ACM Trans. Graph. vol. 26, no. 3, pp.267–276, 2007.
[6] A. O. Aky□z, R. Fleming, B. E. Riecke, E. Reinhard and H. H. B□lthoff, “Do HDR Displays Support LDR Content? A Psychophysical Evaluation”, ACM Transactions on Graphics (SIGGRAPH), vol. 26, no. 3, 2007.
[7] L. Meylan, S. Daly, and S. S□sstrunk, “Tone mapping for high dynamic range displays,” In Electronic Imaging, vol. 6492, 2007.
[8] B. Masia, S. Agustin, R. W. Fleming, O. Sorkine, and D. Gutierrez, “Evaluation of reverse tone mapping through varying exposure conditions,” ACM Trans. Graph, vol.28, no.5, 2009
[9] F. Banterle, P. Ledda, K. Debattista, and A. Chalmers, “Inverse Tone Mapping,” In Proc. of GRAPHITE ’06, pp.349–356, 2006.
[10] W. M. Ke, T. H. Wang, and C. T. Chiu, “Hardware-efficient virtual high dynamic range image reproduction,” IEEE ICIP, 2009.
[11] F. Banterle, P. Ledda, K. Debattista, and A. Chalmers, “Expanding low dynamic range videos for high dynamic range applications,” In Proc. of the Spring Conference on Computer Graphics, ACM, New York, NY, USA, 2008.
[12] L. Wang, L.Y. Wei, K. Zhou, B. Guo, and H.Y. Shum, “High dynamic range image hallucination,” In Proceedings of Eurographics Symposium on Rendering, 2007.
[13] P. Didyk, R. Mantiuk, M. Hein, and H. P. Seidel, “Enhancement of bright video features for HDR displays,” Computer Graphics Forum, vol.27, no. 4, pp. 1265–1274, 2008.
[14] R. Mantiuk, S. Daly, K. Myszkowski, and H. P. Seidel, “Predicting Visible Differences in High Dynamic Range Images - Model and its Calibration,” In: Proc. of Human Vision and Electronic Imaging X, IS&T/SPIE's 17th Annual Symposium on Electronic Imaging, pp. 204-214, 2005.
[15] S. DALY, “The Visible Differences Predictor: An algorithm for the assessment of image fidelity,” In Digital Images and Human Vision, MIT Press, A. B. Watson, Ed., 179–206 , 1993
[16] F. Banterle, P. Ledda, K. Debattista, M. Bloj, A. Artusi, and A. Chalmers. “A psychophysical evaluation of inverse tone mapping techniques,” Computer Graphics Forum, vol.28, no.1, pp.13-25, 2009.
[17] HDR-VDP: http://www.mpi-inf.mpg.de/resources/hdr/vdp/index.html
[18] R. P. Kovaleski, and M. M. Oliveira, “High-quality brightness enhancement functions for real-time reverse tone mapping,” The Visual Computer: International Journal of Computer Graphics, vol.25 no.5-7, pp.539-547, April 2009.
[19] G. Ward, ``A Contrast-Based Scalefactor for Luminance Display'', Graphics Gems IV, Ed. by P. S. Heckbert, pp. 415-421, 1994.
[20] G. W. Larson, H. Rushmeier, and C. Piatko, A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 3, NO. 4, OCTOBER-DECEMBER 1997
[21] T. O. Aydin, R. Mantiuk, K. Myszkowski, and H. P. Seidel, “Dynamic range independent image quality assessment.” ACM Trans.Graph vol.27, no.3, pp.69, 2008.
[22] M. Čad□k, M. Wimmer, L. Neumann, and A. Artusi, “Image Attributes and Quality for Evaluation of Tone Mapping Operators,” In Proceedings of Pacific Graphics, pp. 35-44. Oct, 2006.
[23] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” ACM Transactions on Graphics, vol.21, no.3, pp.267–276, 2002.
[24] G. Deng, “An entropy interpretation of the logarithmic image processing model with application to contrast enhancement,” IEEE Trans. Image Processing, vol. 18, no. 5, pp. 1135-1140, 2009.
[25] D. Hasler and S. S□sstrunk, “Measuring colourfulness in natural images,” Proc. SPIE/IS&T Human Vision and Electronic Imaging, vol. 5007, pp. 87-95 , Jan, 2003.
[26] R. Mantiuk, A. M. Tomaszewska, and W. Heidrich, “Color correction for tone mapping,” Comput. Graph. Forum, vol.28, no.2, pp. 193-202, 2009.
[27] Original Image Sequence of Memorial Church: http://www.debevec.org/Research/HDR/