## 1 Introduction  
A camera is designed to perform an HVS-like task--to capture the surroundings and provide information for higher-level processing. Given this similarity, a naive conception would be that a physical scene captured by a camera and viewed on a display device should invoke the exact same response as observing the scene directly. However, this is very seldom the case, for a number of reasons. First of all, there are insufficient depth cues in the captured image, and there are differences in color and brightness between the captured image and what the HVS perceives. The camera and the HVS also have different dynamic ranges. Therefore, the camera and the display devices are unable to cover wider ranges of luminance which the HVS can detect simultaneously, and this implies that there is more visual information available in the real-life scene than what can be captured and reproduced. For instance, when a camera captures an object in a dark indoor environment in front of a bright window, one has to choose between properly exposed background and foreground, while the other information is lost in dark or saturated image areas, respectively. However, it is usually not a problem for the human eyes to simultaneously register both foreground and background. Hence, the limitation of the camera compared to the human eyes becomes evident. HDR imaging is able to get information in both dark and bright image regions and it matches or outperforms the HVS in its capability to capture the dynamic range. Therefore, to enjoy images that exploit the full potential of HVS, equally good quality cameras and display devices are needed. Fortunately, today, HDR scenes can be efficiently captured by using high-end cameras, new sensor technologies or computational photography methods that can extract HDRIs from several differently exposed low dynamic range images (LDRIs). Yet display technology is lagging behind and a majority of display today has moderate contrast rates and is unable to faithfully reproduce HDR content. To overcome such limitations, tone mapping methods are used to efficiently scale contrast of HDRIs and to reproduce color on standard LDR display devices, without significant loss of important features and detailed information. That is, tone mapping operators (TMOs) are used to compress the dynamic range of HDRIs and thereby fill the gap between the current HDR imaging technique and the limitation of visualizing HDRIs on conventional LDR display devices. TMOs provide an alternative method of HDR display technology. Although HDR display technology is rapidly advancing, there still remains a strong need to realize HDRIs on LDR display devices [1]. Over the past several decades, a lot of research and study were carried out in tone mapping such as enhancement of contrast, brightness, and visibility of LDRI [2; 3; 4]. [PERSON] and his colleagues [5] proposed a photographic practice-based method using the automatic dodging-and-burning technology. Their method provided a practical approach to compressing the dynamic range, but a circular surround restricts the scope of performance, likely causing halos. [PERSON] and [PERSON] [6] applied an Image Color Appearance Model (iCAM) to chromatics, converting the RGB color space into CIE XYZ tristimulus values. However, the conversion caused saturation loss and the resultant darker resulting image. [PERSON] and his colleagues [7] designed an iCAM-based algorithm called iCAM06. The algorithm split HDRI into the base layer and the detail layer, using the piece-wise bilateral filter. However, it had a drawback that the resulting image had color saturation reduction. [PERSON] et al. [8] came up with a new approach to correcting saturation loss and saturation reduction alike, using the inverse compensation in a bid to improve iCAM06. [PERSON] et al. [9] proposed a color correction operator which scales the tone, as well as preserves original image colors.