Abstract
Tone mapping algorithms reproduce high dynamic range (HDR) images on low dynamic range images in the standard display devices such as LCD, CRT, projectors, and printers. In this paper, we propose a statistical clustering-based tone mapping technique that would be able to adapt the local content of an image as well as its color. At first, the HDR image is partitioned into many overlapped color patches and we disintegrate each color patch into three segments: patch mean, color variation and color structure. Then based on the color structure component, the extracted color patches are clustered into a number of clusters by k-means clustering technique. For each cluster, the statistical signal processing technique namely Hessian multi set canonical correlations (HesMCC) has been produced to ascertain the transform matrix. Moreover, the HesMCC are fundamentally utilized for performing the dimensionality reduction of patches and to form effective tone mapped images. Contrasting with the current strategies, the procedures in the proposed clustering-based strategy can better adapt image color and its local structures by exploiting the image in the worldwide repetition. Experimental results show that the running time of the proposed method is less about 88.32%, 92%, 68.9%, and 29.4%, while comparing with other existing tone mapping methods.
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Kottayil, N., Valenzise, G., Dufaux, F., & Cheng, I. (2018). Blind quality estimation by disentangling perceptual and noisy features in high dynamic range images. IEEE Transactions on Image Processing, 27, 1512–1525.
Gao, M., Wee, S., & Jeong, J. (2018). Multiscale decomposition based high dynamic range tone mapping method using guided image filter. In 2018 International conference on network infrastructure and digital content (IC-NIDC), Guiyang, China (pp. 30–34).
Bist, C., Cozot, R., Madec, G., & Ducloux, X. (2017). Tone expansion using lighting style aesthetics. Computers & Graphics, 62, 77–86.
Artusi, A., Mantiuk, R., Richter, T., Korshunov, P., Hanhart, P., Ebrahimi, T., et al. (2016). JPEG XT: A compression standard for HDR and WCG Images [Standards in a Nutshell]. IEEE Signal Processing Magazine, 33, 118–124.
Hel-Or, Y., Hel-Or, H., & David, E. (2014). Matching by tone mapping: Photometric invariant template matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 317–330.
Narwaria, M., Perreira Da Silva, M., Le Callet, P., & Pepion, R. (2014). Tone mapping based HDR compression: Does it affect visual experience? Signal Processing: Image Communication, 29, 257–273.
Boitard, R., Cozot, R., Thoreau, D., & Bouatouch, K. (2014). Zonal brightness coherency for video tone mapping. Signal Processing: Image Communication, 29, 229–246.
Ma, K., Yeganeh, H., Zeng, K., & Wang, Z. (2015). High dynamic range image compression by optimizing tone mapped image quality index. IEEE Transactions on Image Processing, 24, 3086–3097.
Kwon, H., Lee, S., Lee, G., & Sohng, K. (2016). Radiance map construction based on spatial and intensity correlations between LE and SE images for HDR imaging. Journal of Visual Communication and Image Representation, 38, 695–703.
Sikudova, E., Pouli, T., Artusi, A., Akyuz, A., Banterle, F., Mazlumoglu, Z., et al. (2016). A gamut-mapping framework for color-accurate reproduction of HDR Images. IEEE Computer Graphics and Applications, 36, 78–90.
Krasula, L., Narwaria, M., Fliegel, K., & Le Callet, P. (2017). Preference of experience in image tone-mapping: Dataset and framework for objective measures comparison. IEEE Journal of Selected Topics in Signal Processing, 11, 64–74.
Duan, J., Bressan, M., Dance, C., & Qiu, G. (2010). Tone-mapping high dynamic range images by novel histogram adjustment. Pattern Recognition, 43, 1847–1862.
Mai, Z., Mansour, H., Mantiuk, R., Nasiopoulos, P., Ward, R., & Heidrich, W. (2011). Optimizing a tone curve for backward-compatible high dynamic range image and video compression. IEEE Transactions on Image Processing, 20, 1558–1571.
Čadík, M., Wimmer, M., Neumann, L., & Artusi, A. (2008). Evaluation of HDR tone mapping methods using essential perceptual attributes. Computers & Graphics, 32, 330–349.
Inan, U. (2002). Lightning effects at high altitudes: Sprites, elves, and terrestrial gamma ray flashes. Comptes Rendus Physique, 3, 1411–1421.
Su, Z., Zeng, B., Miao, J., Luo, X., Yin, B., & Chen, Q. (2018). Relative reductive structure-aware regression filter. Journal of Computational and Applied Mathematics, 329, 244–255.
Gu, B., Li, W., Zhu, M., & Wang, M. (2013). Local edge-preserving multiscale disintegration for high dynamic range image tone mapping. IEEE Transactions on Image Processing, 22, 70–79.
Liu, W., Yang, X., Tao, D., Cheng, J., & Tang, Y. (2018). Multiview dimension reduction via Hessian multiset canonical correlations. Information Fusion, 41, 119–128.
Ok, J., & Lee, C. (2017). HDR tone mapping algorithm based on difference compression with adaptive reference values. Journal of Visual Communication and Image Representation, 43, 61–76.
Khan, I., Rahardja, S., Khan, M., Movania, M., & Abed, F. (2018). A tone-mapping technique based on histogram using a sensitivity model of the human visual system. IEEE Transactions on Industrial Electronics, 65, 3469–3479.
Kovács, G. (2017). Matching by monotonic tone mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1424–1436.
Kinoshita, Y., & Kiya, H. (2019). Deep inverse tone mapping using LDR based learning for estimating HDR images with absolute luminance. arXiv preprint arXiv:1903.01277.
Thai, B. C., Mokraoui, A., & Matei, B. (2019). Contrast enhancement and details preservation of tone mapped high dynamic range images. Journal of Visual Communication and Image Representation, 58, 589–599.
Hanhart, P., & Ebrahimi, T. (2017). Evaluation of JPEG XT for high dynamic range cameras. Signal Processing: Image Communication, 50, 9–20.
El Mezeni, D., & Saranovac, L. (2018). Enhanced local tone mapping for detail preserving reproduction of high dynamic range images. Journal of Visual Communication and Image Representation, 53, 122–133.
Benzi, M., Escobar, M., & Kornprobst, P. (2018). A bio-inspired synergistic virtual retina model for tone mapping. Computer Vision and Image Understanding, 168, 21–36.
Jiang, Q., Shao, F., Lin, W., & Jiang, G. (2017). BLIQUE-TMI: Blind quality evaluator for tone-mapped images based on local and global feature analyses. IEEE Transactions on Circuits and Systems for Video Technology, 29(2), 323–335.
Artusi, A., Pouli, T., Banterle, F., & Oğuz Akyüz, A. (2018). Automatic saturation correction for dynamic range management algorithms. Signal Processing: Image Communication, 63, 100–112.
Artusi, A., Mantiuk, R. K., Richter, T., Hanhart, P., Korshunov, P., Agostinelli, M., et al. (2019). Overview and evaluation of the JPEG XT HDR image compression standard. Journal of Real-Time Image Processing, 16(2), 413–428.
Li, Z., & Zheng, J. (2014). Visual-salience-based tone mapping for high dynamic range images. IEEE Transactions on Industrial Electronics, 61, 7076–7082.
Agaian, S., Silver, B., & Panetta, K. (2007). Transform coefficient histogram-based image enhancement algorithms using contrast entropy. IEEE Transactions on Image Processing, 16, 741–758.
Jose, A., & Heisterklaus, I. (2017). Bag of fisher vectors representation of images by saliency-based spatial partitioning. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), New Orleans, LA, USA (pp. 1762–1766).
Lau, C., Heidrich, W., & Mantiuk, R. (2011). Cluster-based color space optimizations. In 2011 International conference on computer vision, Barcelona, Spain (pp. 1172–1179).
Al-Mohair, H., Mohamad Saleh, J., & Suandi, S. (2015). Hybrid human skin detection using neural network and k-means clustering technique. Applied Soft Computing, 33, 337–347.
Wu, X., Zhu, X., Wu, G., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26, 97–107.
Lanjewar, R., Mathurkar, S., & Patel, N. (2015). Implementation and comparison of speech emotion recognition system using Gaussian mixture model (GMM) and k-nearest neighbor (K-NN) techniques. Procedia Computer Science, 49, 50–57.
Takashi, S., Masayuki, T., & Masatoshi, O. (2016). Gradient-domain image reconstruction framework with intensity-range and base-structure constraints. In IEEE conference on computer vision and pattern recognition, Las Vegas, Nevada, Available at: http://www.ok.ctrl.titech.ac.jp/res/IC/ProxPoisson/ProxPoisson.html.
Shan, Q., Jia, J., & Brown, M. (2010). Globally optimized linear windowed tone mapping. IEEE Transactions on Visualization and Computer Graphics, 16, 663–675.
Shibata, T., Tanaka, M., & Okutomi, M. (2016). Gradient-domain image reconstruction framework with intensity-range and base-structure constraints. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2745–2753).
Li, H., Jia, X., & Zhang, L. (2018). Clustering based content and color adaptive tone mapping. Computer Vision and Image Understanding, 168, 37–49.
Mantiuk, R., Daly, S., & Kerofsky, L. (2008). Display adaptive tone mapping. ACM Transactions on Graphics, 27, 68.
Neelima, N., & Kumar, Y. R. (2019). Optimal clustering based outlier detection and cluster center initialization algorithm for effective tone mapping. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-019-07907-4.
An, G., Lee, S., Ahn, Y., & Kang, S. (2018). 23-3: Deep tone-mapped HDRNET for high dynamic range image restoration. SID Symposium Digest of Technical Papers, 49, 291–294.
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Neelima, N., Kumar, Y.R. Tone Mapping High Dynamic Range Images by Hessian Multiset Canonical Correlations. Sens Imaging 21, 8 (2020). https://doi.org/10.1007/s11220-020-0271-x
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DOI: https://doi.org/10.1007/s11220-020-0271-x