We propose a fast, high quality tone mapping technique to display high contrast images on devices with limited dynamic range of luminance values. The method is based on logarithmic compression of luminance values, imitating the human response to light. A bias power function is introduced to adaptively vary logarithmic bases, resulting in good preservation of details and contrast. To improve contrast in dark areas, changes to the gamma correction procedure are proposed. Our adaptive logarithmic mapping technique is capable of producing perceptually tuned images with high dynamic content and works at interactive speed. We demonstrate a successful application of our tone mapping technique with a high dynamic range video player enabling to adjust optimal viewing conditions for any kind of display while taking into account user preference concerning brightness, contrast compression, and detail reproduction.

High dynamic range (HDR) imaging is an area of increasing importance, but most display devices still have limited dynamic range (LDR). Various techniques have been proposed for compressing the dynamic range while retaining important visual information. Multiscale image processing techniques, which are widely used for many image processing tasks, have a reputation of causing halo artifacts when used for range compression. However, we demonstrate that they can work when properly implemented. We use a symmetrical analysis-synthesis filter bank, and apply local gain control to the subbands. We also show that the technique can be adapted for the related problem of “companding”, in which an HDR image is converted to an LDR image, and later expanded back to high dynamic range.

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.

New imaging and rendering systems commonly use physically accurate lighting information in the form of highdynamic range (HDR) images and video. HDR images contain actual colorimetric or physical values, which can span 14 orders of magnitude, instead of 8-bit renderings, found in standard images. The additional precision and quality retained in HDR visual data is necessary to display images on advanced HDR display devices, capable of showing contrast of 50,000:1, as compared to the contrast of 700:1 for LCD displays. With the development of high-dynamic range visual techniques comes a need for an automatic visual quality assessment of the resulting images. In this paper we propose several modifications to the Visual Difference Predicator (VDP). The modifications improve the prediction of perceivable differences in the full visible range of luminance and under the adaptation conditions corresponding to real scene observation. The proposed metric takes into account the aspects of high contrast vision, like scattering of the light in the optics (OTF), nonlinear response to light for the full range of luminance, and local adaptation. To calibrate our HDR VDP we perform experiments using an advanced HDR display, capable of displaying the range of luminance that is close to that found in real scenes.

A number of successful tone mapping operators for contrast compression have been proposed due to the need to visualize high dynamic range (HDR) images on low dynamic range devices. They were inspired by fields as diverse as image processing, photographic practice, and modeling of the human visual systems (HVS). The variety of approaches calls for a systematic perceptual evaluation of their performance. We conduct a psychophysical experiment based on a direct comparison between the appearance of real-world scenes and HDR images of these scenes displayed on a low dynamic range monitor. In our experiment, HDR images are tone mapped by seven existing tone mapping operators. The primary interest of this psychophysical experiment is to assess the differences in how tone mapped images are perceived by human observers and to find out which attributes of image appearance account for these differences when tone mapped images are compared directly with their corresponding realworld scenes rather than with each other. The human subjects rate image naturalness, overall contrast, overall brightness, and detail reproduction in dark and bright image regions with respect to the corresponding real-world scene. The results indicate substantial differences in perception of images produced by individual tone mapping operators. We observe a clear distinction between global and local operators in favor of the latter, and we classify the tone mapping operators according to naturalness and appearance attributes. Keywords: high dynamic range (HDR) images, human visual systems (HVS), tone mapping, psychophysics, ANOVA, correlation, MANOVA, Mahalanobis distances. 1.

Since new imaging and rendering systems commonly use physically accurate lighting information in the form of High-Dynamic Range data, there is a need for an automatic visual quality assessment of the resulting images. In this work we extend the Visual Difference Predictor (VDP) developed by Daly to handle HDR data. This let us predict if a human observer is able to perceive differences for a pair of HDR images under the adaptation conditions corresponding to the real scene observation.

The problem of reproducing high dynamic range images on devices with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color vision, physiological aspects, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this contribution, we present an overview about the effects of basic image attributes in HDR tone mapping. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical experiments that we have performed to prove the proposed relationship scheme. Moreover, we also present an evaluation of existing tone mapping methods (operators) with regard to these attributes. Finally, the execution of with-reference and without a real reference perceptual experiments gave us the opportunity to relate the obtained subjective results. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping method, but it also sets the stage for well-founded quality comparisons between tone mapping methods. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible.

Raw data HDR image (pseudo color encoded) Tone mapped image Figure 1: Pipeline of high dynamic range volume visualization. The input is a scalar volume with high precision and/or high resolution (e.g. 2048 3). The user defines a transfer function using a novel non-linear magnification interface. The volume rendering output is in high dynamic range image format. By applying a tone mapping operator, the final result can be displayed on a regular low dynamic range display device. High resolution volumes require high precision compositing to preserve detailed structures. This is even more desirable for volumes with high dynamic range values. After the high precision intermediate image has been computed, simply rounding up pixel values to regular display scales loses the computed details. In this paper, we present a novel high dynamic range volume visualization method for rendering volume data with both high spatial and intensity resolutions. Our method performs high precision volume rendering followed by dynamic tone mapping to preserve details on regular display devices. By leveraging available high dynamic range image

A method for fusing multi-exposure images of a static scene taken by a stationary camera into an image with maximum information content is introduced. The method partitions the image domain into uniform blocks and for each block selects the image that contains the most information within that block. The selected images are then blended together using monotonically decreasing blending functions that are centered at the blocks and have a sum of 1 everywhere in the image domain. The optimal block size and width of the blending functions are determined using a gradient-ascent algorithm to maximize information content in the fused image.

Global illumination dramatically improves realistic appearance of rendered scenes, but usually it is neglected in VR systems due to its high costs. In this work we present an efficient global illumination solution specifically tailored for those CAVE applications, which require an immediate response for dynamic light changes and allow for free motion of the observer, but involve scenes with static geometry. As an application example we choose the car interior modeling under free driving conditions. We illuminate the car using dynamically changing High Dynamic Range (HDR) environment maps and use the Precomputed Radiance Transfer (PRT) method for the global illumination computation. We leverage the PRT method to handle scenes with non-trivial topology represented by complex meshes. Also, we propose a hybrid of PRT and final gathering approach for high-quality rendering of objects with complex Bi-directional Reflectance Distribution Function (BRDF). We use this method for predictive rendering of the navigation LCD panel based on its measured BRDF. Since the global illumination computation leads to HDR images we propose a tone mapping algorithm tailored specifically for the CAVE. We employ head tracking to identify the observed screen region and derive for it proper luminance adaptation conditions, which are then used for tone mapping on all walls in the CAVE. We distribute our global illumination and tone mapping computation on all CPUs and GPUs available in the CAVE, which enables us to achieve interactive performance even for the costly final gathering approach.