用户名: 密码: 验证码:
A Unified Tone Mapping Operation for HDR Images Including Both Floating-Point and Integer Data
详细信息    查看全文
  • 关键词:High dynamic range ; Tone mapping ; Unified ; Integer ; Floating ; point
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2015
  • 出版时间:2015
  • 年:2015
  • 卷:9314
  • 期:1
  • 页码:321-333
  • 全文大小:698 KB
  • 参考文献:1.Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. ACM Trans. Graph. 21(3), 267–276 (2002)CrossRef
    2.Reinhard, E., Ward, G., Pattanaik, S., Debevec, P., Heidrich, W., Myszkowski, K.: High Dynamic Range Imaging - Acquisition, Display and Image based Lighting. Morgan Kaufmann, Burlington (2010)
    3.Drago, F., Myszkowski, K., Annen, T., Chiba, N.: Adaptive logarithmic mapping for displaying high contrast scenes. Comput. Graph. Forum 22(3), 419–426 (2003)CrossRef
    4.Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002)CrossRef
    5.Iwahashi, M., Kiya, H.: Efficient lossless bit depth scalable coding for HDR images. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), no.OS.37-IVM.16-4 (2013)
    6.Iwahashi, M., Kiya, H.: Two layer lossless coding of HDR images. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1340–1344 (2013)
    7.Xu, R., Pattanaik, S.N., Hughes, C.E.: High-dynamic-range still image encoding in JPEG2000. IEEE Trans. Comput. Graph. Appl. 25(6), 57–64 (2005)CrossRef
    8.Zhang, Y., Reinhard, E., Bull, D.: Perception-based high dynamic range video compression with optimal bit-depth transformation. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1321–1324 (2011)
    9.Iwahashi, M., Yoshida, T., Mokhtar, N.B., Kiya, H.: Bit-depth scalable lossless coding for high dynamic range images. EURASIP J. Adv. Sig. Process. 2015, 22 (2015)CrossRef
    10.Thakur, S.K., Sivasubramanian, M., Nallaperumal, K., Marappan, K., Vishwanath, N.: Fast tone mapping for high dynamic range images. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4 (2013)
    11.Duan, J., Qiu, G: Fast tone mapping for high dynamic range images. In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 847–850 (2004)
    12.Murofushi, T., Iwahashi, M., Kiya, H.: An integer tone mapping operation for HDR images expressed in floating point data. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2479–2483 (2013)
    13.Dobashi, T., Murofushi, T., Iwahashi, M., Kiya, H.: A fixed-point tone mapping operation for HDR images in the RGBE format. In: Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), no.OS.37-IVM.16-4 (2013)
    14.Dobashi, T., Murofushi, T., Iwahashi, M., Kiya, H.: A fixed-point global tone mapping operation for HDR images in the RGBE format. IEICE Trans. Fundam. E97–A(11), 2147–2153 (2014)CrossRef
    15.Lampert, C.H., Wirjadi, O.: Anisotropic gaussian filtering using fixed point arithmetic. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 1565–1568 (2006)
    16.Chang, W.-H., Nguyen, T.Q.: On the fixed-point accuracy analysis of FFT algorithm. IEEE Trans. Sig. Process. 56(10), 4673–4682 (2008)MathSciNet CrossRef
    17.Rocher, R., Menard, D., Scalart, P., Sentieys, O.: Analytical approach for numerical accuracy estimation of fixed-point systems based on smooth operations. IEEE Trans. Circ. Syst. Part-I 59(10), 2326–2339 (2012)MathSciNet
    18.Murofushi, T., Dobashi, T., Iwahashi, M., Kiya, H.: An integer tone mapping operation for HDR images in OpenEXR with denormalized numbers. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), no.TEC-P10.6 (2014)
    19.Dobashi, T., Tashiro, A., Iwahashi, M., Kiya, H.: A fixed-point implementation of tone mapping operation for HDR images expressed in floating-point format. APSIPA Trans. Sig. Inf. Process. 3(11), 1–11 (2004)
    20.Ward, G.: Real pixels. In: Arvo, J. (ed.) Graphic Gems 2, pp. 80–83. Academic Press, San Diego (1992)
    21.Kainz, F., Bogart, R., Hess, D.: The OpenEXR image file format. In: ACM SIGGRAPH Technical Sketches & Applications (2003)
    22.Information technology - Microprocessor Systems - Floating-Point arithmetic. ISO/IEC/IEEE 60559 (2011)
    23.Wang, Z., Bovik, A.C., Seikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRef
  • 作者单位:Toshiyuki Dobashi (18)
    Masahiro Iwahashi (19)
    Hitoshi Kiya (18)

    18. Tokyo Metropolitan University, Tokyo, Japan
    19. Nagaoka University of Technology, Niigata, Japan
  • 丛书名:Advances in Multimedia Information Processing -- PCM 2015
  • ISBN:978-3-319-24075-6
  • 刊物类别:Computer Science
  • 刊物主题:Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1611-3349
文摘
This paper considers a unified tone mapping operation (TMO) for HDR images. This paper includes not only floating-point data but also long-integer (i.e. longer than 8-bit) data as HDR image expression. A TMO generates a low dynamic range (LDR) image from a high dynamic range (HDR) image by compressing its dynamic range. A unified TMO can perform tone mapping for various HDR image formats with a single common TMO. The integer TMO which can perform unified tone mapping by converting an input HDR image into an intermediate format was proposed. This method can be executed efficiently with low memory and low performance processor. However, only floating-point HDR image formats have been considered in the unified TMO. In other words, a long-integer which is one of the HDR image formats has not been considered in the unified TMO. This paper extends the unified TMO to a long-integer format. Thereby, the unified TMO for all possible HDR image formats can be realized. The proposed method ventures to convert a long-integer number into a floating-point number, and treats it as two 8-bit integer numbers which correspond to its exponent part and mantissa part. These two integer numbers are applied the tone mapping separately. The experimental results shows the proposed method is effective for an integer format in terms of the resources such as the computational cost and the memory cost.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700