摘要(英) |
Shooting a good picture requires a lots of photography skills and good equipment. Thankfully, modern technology can make up for non-professionals. If someone taking a picture using the wrong exposure setting or the camera is not good enough to reproduce the scene, the photo produced would be in low contrast and no color. Thankfully, image enhancement technique can retrieve the lost detail and color information. However, image enhancement cannot deal with image that has lost almost all detail information. HDR technique is a solution to this kind of problem.
There are many ways to generate a HDR image. They are mainly about how to do tone reproduction or how to do the exposure fusion. Others are focus on removing ghost effect. However, there are rarely studies which are related to people′s feeling and the preferences to the HDR images. In other words, we have few research about what kinds of images (HDR images) are the good images depend on subjective feeling while we have many ways to generate all kinds of different HDR images. Hence, in this paper, we want to find out whether there is an index can reflect subjective feeling to HDR image. The method we proposed is call S_fpg. The foreground pixel used in image enhancement to evaluate the performance is used to find out the detail region of a HDR image because image enhancement and HDR are basically the same thing-retrieve the detail information from scene to image. Once finding out the foreground, the saturation measure is evolved to see the colorfulness of HDR image.
A subjective study about HDR images preferences survey is conducted using online survey system. We conduct two kinds of survey to get the preferences data. One is choose-the-best-one and the other is two alternative forced choice. The reason we do choose-the-best-one test is that the conventional mean opinion score survey cause too much cognitive load to the testers. Furthermore, the score testers graded doesn’t have a standard. Ranking the preference image is another method. However, the testers have difficulty in ranking the image that they don’t like. Hence, we change the survey method to choose-the-best-one which significantly reduce the cognitive load to testers. Two alternative forced choice is suggested to be more intuitive and hence, we conduct another survey using 2AFC.
Both of two results are consist with our index. The index is validated by correlation coefficient with subjective test using Spearman′s rank correlation coefficient and Pearson correlation coefficient. The results show that our method is highly correlated with subjective preferences comparing with other objective measure metrics. |
參考文獻 |
[1] Debevec, Paul E., and Jitendra Malik. "Recovering high dynamic range radiance maps from photographs." ACM SIGGRAPH 2008 classes. ACM, 2008.
[2] Mertens, Tom, Jan Kautz, and Frank Van Reeth. "Exposure fusion: A simple and practical alternative to high dynamic range photography." Computer Graphics Forum. Vol. 28. No. 1. Blackwell Publishing Ltd, 2009.
[3] Wei-Rong Xie and Chiou-Ting Hsu, “Eposure Fusion of Image Pair without Alignment”, 2013
[4] Hulusic, Vedad, et al. "Perceived dynamic range of HDR images." Quality of Multimedia Experience (QoMEX), 2016 Eighth International Conference on. IEEE, 2016.
[5] Krasula, Lukas, et al. "Influence of HDR reference on observers preference in tone-mapped images evaluation." Quality of Multimedia Experience (QoMEX), 2015 Seventh International Workshop on. IEEE, 2015.
[6] Ghimire, Deepak, and Joonwhoan Lee. "Nonlinear transfer function-based local approach for color image enhancement." IEEE Transactions on Consumer Electronics 57.2 (2011).
[7] Hasler, David, and Sabine Süsstrunk. "Measuring colourfulness in natural images." Proc. IST/SPIE Electronic Imaging 2003: Human Vision and Electronic Imaging VIII. Vol. 5007. No. LCAV-CONF-2003-019. 2003.
[8] Singh, Gajendra, Arun Khosla, and Md Imtiyaz Anwar. "Spatial domain color image enhancement based on local processing." Signal Processing and Integrated Networks (SPIN), 2016 3rd International Conference on. IEEE, 2016.
[9] Hu, Jun, et al. "HDR deghosting: How to deal with saturation?." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013.
[10] Fattal, Raanan, Dani Lischinski, and Michael Werman. "Gradient domain high dynamic range compression." ACM Transactions on Graphics (TOG). Vol. 21. No. 3. ACM, 2002.
[11] Lin, James, and Ivan V. Bajić. "A platform for subjective image quality evaluation on mobile devices." Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on. IEEE, 2016.
[12] D. M. Green and J. A. Swets, Signal Detection Theory and Psychophysics, Peninsula Pub, 1989.
[13] Hadizadeh, Hadi, et al. "Good-looking green images." Image Processing (ICIP), 2011 18th IEEE International Conference on. IEEE, 2011.
[14] Hadizadeh, Hadi, and Ivan V. Bajic. "Saliency-aware video compression." IEEE Transactions on Image Processing 23.1 (2014): 19-33..
[15] Kundu, Debarati, et al. "No-reference image quality assessment for high dynamic range images." Signals, Systems and Computers, 2016 50th Asilomar Conference on. IEEE, 2016.
[16] Hanhart, Philippe, et al. "Benchmarking of objective quality metrics for HDR image quality assessment." EURASIP Journal on Image and Video Processing 2015.1 (2015): 39.
[17] Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3 (1987): 355-368. |