A classic photographic task is the mapping of the potentially high dynamic range of real world luminances to the low dynamic range of the photographic print. This tone reproduction problem is also faced by computer graphics practitioners who map digital images to a low dynamic range print or screen. The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator. In particular, we use and extend the techniques developed by Ansel Adams to deal with digital images. The resulting algorithm is simple and produces good results for a wide variety of images.

High contrast scenes are difficult to depict on low contrast displays without loss of important fine details and textures. Skilled artists preserve these details by drawing scene contents in coarseto-fine order using a hierarchy of scene boundaries and shadings. We build a similar hierarchy using multiple instances of a new low curvature image simplifier (LCIS), a partial differential equation inspired by anisotropic diffusion. Each LCIS reduces the scene to many smooth regions that are bounded by sharp gradient discontinuities, and a single parameter K chosen for each LCIS controls region size and boundary complexity. With a few chosen K values (K1>K2>K3:::) LCIS makes a set of progressively simpler images, and image differences form a hierarchy of increasingly important details, boundaries and large features. We construct a high detail, low contrast display image from this hierarchy by compressing only the large features, then adding back all small details. Unlike linear filter hierarchies such as wavelets, filter banks, or image pyramids, LCIS hierarchies do not smooth across scene boundaries, avoiding “halo ” artifacts common to previous contrast reducing methods and some tone reproduction operators. We demonstrate LCIS effectiveness on several example images.

Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.

Abstract—A common task in computer graphics is the mapping of digital high dynamic range images to low dynamic range display devices such as monitors and printers. This task is similar to the adaptation processes which occur in the human visual system. Physiological evidence suggests that adaptation already occurs in the photoreceptors, leading to a straightforward model that can be easily adapted for tone reproduction. The result is a fast and practical algorithm for general use with intuitive user parameters that control intensity, contrast, and level of chromatic adaptation, respectively. Index Terms—Tone reproduction, dynamic range reduction, photoreceptor physiology. 1

In the real world, the human eye is confronted with a wide range of luminances from bright sunshine to low night light. Our eyes cope with this vast range of intensities by adaptation; changing their sensitivity to be responsive at di#erent illumination levels. This adaptation is highly localized, allowing us to see both dark and bright regions of a high dynamic range environment. In this paper we present a new model of eye adaptation based on physiological data. The model, which can be easily integrated into existing renderers, can function either as a static local tone mapping operator for single high dynamic range image, or as a temporal adaptation model taking into account time elapsed and intensity of preadaptation for a dynamic sequence. We finally validate our technique with a high dynamic range display and a psychophysical study.

Abstract- This paper proposes an approach for pixel transformation of the displayed image to increase the potential energy saving of the backlight scaling method. The proposed approach takes advantage of human visual system characteristics and tries to minimize distortion between the perceived brightness values of the individual pixels in the original image and those of the backlight-scaled image. This is in contrast to previous backlight scaling approaches which simply match the luminance values of the individual pixels in the original and backlight-scaled images. Moreover, the proposed dynamic backlight scaling approach, which is based on tone mapping, is amenable to highly efficient hardware realization because it does not need information about the histogram of the displayed image. Experimental results show that the dynamic tone mapping for backlight scaling method results in about 35 % power saving with an effective distortion rate of 5 % and 55 % power saving for a 20 % distortion rate.

Tone Mapping Operators are used to compress a large range of pixel luminances into a smaller range that is suitable for display on devices with limited dynamic range. This work presents effective and easy to use Tone Mapping Operator based on last ideas in this direction. The estimation process uses sampling method. Essential attention was devoted to providing robustness of algorithm parameter estimation, especially critical for animation applications. The resulting operator produces good images and practically does not require manual parameter tuning.

This paper introduces the foundations of physically accurate rendering in computer graphics. As graphics hardware and processing power improves, we begin to create images in real time that rival real world photography. This paper lays the groundwork for creating such images, working from the most critical component of realistic image generation, the rendering equation. This paper reviews all stages of realistic rendering and physically accurate image generation: description of reflectance properties of materials; solving the rendering equation using Monte Carlo integration; tone reproduction operators to realistically show the images on limited dynamic range displays; and perceptual techniques that can be used to accelerate rendering. 1.

The standard technique for making images viewed at daytime lighting levels look like images of night scenes is to use a low overall contrast, low overall brightnesses, desaturation, and to give the image a "blue shift". This paper introduces two other important e#ects associated with viewing real night scenes: visible noise, and the loss of acuity with little corresponding perceived blur.

—Tone mapping operators are used to compress a large range of pixel luminances into a smaller range, which can be displayed on a monitor screen. In this paper, an effective and easy-to-use tone mapping operator based on the latest ideas developed in this field is presented. The parameter estimation process relies on the sampling method. Special attention is given to the robustness of the algorithm for the parameter estimation. The suggested tone mapping operator ensures good quality of images and almost does not require manual parameter tuning. 1.