## 1 Introduction
[PERSON] and his colleagues [10] proposed a color rendering operator to address computational complexity of the above-mentioned approaches. So, the computational cost sharply decreased, but the resulting image still appeared darker. [PERSON] and his colleagues [11] published a color rendering operator which uses global tone mapping to compress dynamic range. The method made good progress in cutting color or hue shift and color leakage compared to previous methods. However, a well-known problem occurred in that the detailed information was lost in the given HDR image, caused by the global TMO. Recently, [PERSON] et al. [12] proposed a method to convert HDR images into LDR image using histogram clipping approach and gamma correction. Still, the detailed information was lost in the given HDR image. [PERSON] [13] proposed various guided image editing methods. The resulting images were smoothed and contaminated with noises. Another method proposed by [PERSON] et al. [14] employed a function of cone response. Global tone mapping method suggested by [PERSON] et al. [15] uses a single parameter. In this regard, this paper proposes a novel approach to tone mapping of HDRIs, which has two parts: the chromatics-based TMO and the chromatic adaptive transform (CAT). An image histogram (IH), used in this new approach, is a representation of the pixel value distribution based on the given HDRIs and has several strengths such as image without noise, tolerance to image rotation, size variation, and more. The CCH using an IH has long been published as a technology to compress image and video, conduct visual search and recognize objects by selecting necessary features from the given image. In addition, the CCH-based visual saliency detection is used to estimate the attentional gaze of observers viewing a scene, which is faster and easier to implement and involves minimal parameter tuning. Therefore, the TMO in the proposed method adopts CCH-based saliency aware weighting and edge aware weighting methods. The two methods combined with a guided image filter to create a MGIF which thereby decomposes an image into a base layer and a detail layer. The base layer is used to acquire global tone mapping, which contributes to compressing the dynamic range, as well as preserving sharp edges in the given HDRIs. The proposed TMO is a combination of two parameters [16; 17]: key value of scene and visual gamma. Key value of scene is intended to scale the contrast (or brightness) of the given HDRI whereas visual gamma is designed to compress the dynamic range based on the contrast sensitivity function (CSF) [18]. In this way, they contribute to reducing the luminance shift and hue shift (or color shift), as well as controlling the dynamic range according to the given HDRIs. As a result, the parameters, key value of scene and visual gamma, are automatically set. After the performance of TMO, the CAT is used to cope with mismatch between real-world scenes and displayed images. Instead of using the fixed luminance value (\(F=1\) for average, \(F=0.8\) for dim and dark) per se from the iCAM06 [7], the CAT in the proposed method modifies the luminance factor to avoid color leakage and color shift (or hue shift). The proposed method mainly contributes to reducing halos and avoiding loss of detailed information without color or hue shift and color leakage in the given HDRIs.