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[Author] Toshiyuki DOBASHI(2hit)

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  • A Unified Tone Mapping Operation for HDR Images Expressed in Integer Data

    Toshiyuki DOBASHI  Masahiro IWAHASHI  Hitoshi KIYA  

     
    LETTER-Image

      Vol:
    E99-A No:3
      Page(s):
    774-776

    This letter considers a unified tone mapping operation (TMO) for HDR images. The unified TMO can perform tone mapping for various HDR image formats with a single common operation. The integer TMO which can realize unified tone mapping by converting an input HDR image into an intermediate format is 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 method. In other words, a long-integer which is one of the HDR image formats has not been considered in the method. This letter applies the method to a long-integer format, and confirm its performance. The experimental results show the proposed method is effective for an integer format in terms of the resources such as the computational cost and the memory cost.

  • A Fixed-Point Global Tone Mapping Operation for HDR Images in the RGBE Format

    Toshiyuki DOBASHI  Tatsuya MUROFUSHI  Masahiro IWAHASHI  Hitoshi KIYA  

     
    PAPER

      Vol:
    E97-A No:11
      Page(s):
    2147-2153

    A global tone mapping operation (TMO) for high dynamic range (HDR) images with fixed-point arithmetic is proposed and evaluated in this paper. A TMO generates a low dynamic range (LDR) image from an HDR image by compressing its dynamic range. Since an HDR image is generally expressed in a floating-point data format, a TMO also deals with floating-point data even though a resultant LDR image is integer data. The proposed method treats a floating-point number as two 8-bit integer numbers which correspond to an exponent part and a mantissa part, and applies tone mapping to these integer numbers separately. Moreover, the method conducts all calculations in the tone mapping with only fixed-point arithmetic. As a result, the method reduces a memory cost and a computational cost. The evaluation shows that the proposed method reduces 81.25% of memory usage. The experimental results show that the processing speed of the proposed method with fixed-point arithmetic is 23.1 times faster than the conventional method with floating-point arithmetic. Furthermore, they also show the PSNR of LDR images obtained by the proposed method are comparable to those of the conventional method, though reducing computational and memory cost.