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.

brightness enhancement function computed by our method. Center right: two virtual exposures of the resulting HDR image with a contrast of 9300: 1. Right: the same image shown on an HDR display, using a 10 % semi-transparent filter to show details in bright regions. New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

To embrace the imminent transition from traditional low-contrast video (LDR) content to superior high dynamic range (HDR) content, we propose a novel backward-compatible HDR video compression (HDR MPEG) method. We introduce a compact reconstruction function that is used to decompose an HDR video stream into a residual stream and a standard LDR stream, which can be played on existing MPEG decoders, such as DVD players. The reconstruction function is finely tuned to the content of each HDR frame to achieve strong decorrelation between the LDR and residual streams, which minimizes the amount of redundant information. The size of the residual stream is further reduced by removing invisible details prior to compression using our HDR-enabled filter, which models luminance adaptation, contrast sensitivity, and visual masking based on the HDR content. Designed especially for DVD movie distribution, our HDR MPEG compression method features low storage requirements for HDR content resulting in a 30 % size increase to an LDR video sequence. The proposed compression method does not impose restrictions or modify the appearance of the LDR or HDR video. This is important for backward compatibility of the LDR stream with current DVD appearance, and also enables independent fine tuning, tone mapping, and color grading of both streams.

Abstract not found

A recently developed quantitative model describing the dynamical response characteristics of primate cones is used for rendering high dynamic range (HDR) video. The model provides range compression, as well as luminance-dependent noise suppression. The steady-state (static) version of the model provides a global tone mapping algorithm for rendering HDR images. Both the static and the dynamic cone models can be inverted, enabling the expansion of HDR images and video that were compressed with the cone model.

Many real-world scenes contain a dynamic range that exceeds conventional display technology by several orders of magnitude. Through the combination of several existing technologies, new high dynamic range displays, capable of reproducing a range of intensities much closer to that of real environments, have been constructed. These benefits come at the cost of more optically complex devices; involving two image modulators, controlled in unison, to display images. We present several methods of rendering images to this new class of devices for reproducing photometrically accurate images. We discuss the process of calibrating a display, matching the response of the device with our ideal model. We then derive series of methods for efficiently displaying images, optimized for different criteria and evaluate them in a perceptual framework.

High dynamic range (HDR) images are increasingly employed in games and interactive applications for accurate rendering and illumination. One disadvantage of HDR images is their large data size; unfortunately, even though solutions have been proposed for future hardware, commodity graphics hardware today does not provide any native compression for HDR textures. In this paper, we perform extensive study of possible methods for supporting compressed HDR textures on commodity graphics hardware. A desirable solution must be implementable on DX9 generation hardware, as well as meet the following requirements. First, the data size should be small and the reconstruction quality must be good. Second, the decompression must be efficient; in particular, bilinear/trilinear/anisotropic texture filtering ought to be performed via native texture hardware instead of custom pixel shader filtering. We present a solution that optimally meets these requirements. Our basic idea is to convert a HDR texture to a custom LUVW space followed by an encoding into a pair of 8-bit DXT textures. Since DXT format is supported on modern commodity graphics hardware, our approach has wide applicability. Our compression ratio is 3:1 for FP16 inputs, allowing applications to store 3 times the number of HDR texels in the same memory footprint. Our decompressor is efficient and can be implemented as a short pixel program. We leverage existing texturing hardware for fast decompression and native texture filtering, allowing HDR textures to be utilized just like traditional 8-bit DXT textures. Our reduced data size has a further advantage: it is even faster than rendering from uncompressed HDR textures due to our reduced texture memory access. Given the quality and efficiency, we believe our approach suitable for games and interactive applications.

Most common image and video formats have been designed to work with existing output devices, like LCD or CRT monitors. As display technology makes progress, these formats no longer represent the data that new devices can display. Therefore a shift towards higher precision image and video formats is imminent.

In this paper, we present a learning-based image processing technique. We have developed a novel method to map high dynamic range scenes to low dynamic range images for display in standard (low dynamic range) reproduction media. We formulate the problem as a quantization process and employ an adaptive conscience learning strategy to ensure that the mapped low dynamic range displays not only faithfully reproduce the visual features of the original scenes, but also make full use of the available display levels. This is achieved by the use of a competitive learning neural network that employs a frequency sensitive competitive learning mechanism to adaptively design the quantizer. By optimizing an L2 distortion function, we ensure that the mapped low dynamic images preserve the visual characteristics of the original scenes. By incorporating a frequency sensitive competitive mechanism, we facilitate the full utilization of the limited displayable levels. We have developed a deterministic and practicable learning procedure which uses a single variable to control the display result. We give a detailed description of the implementation procedure of the new learning-based high dynamic range compression method and present experimental results to demonstrate the effectiveness of the method in displaying a variety of high dynamic range scenes.

This work studies an optimization approach for designing tone reproduction curve (TRC) based tone mapping operators for the display of high dynamic range (HDR) images in low dynamic range (LDR) reproduction media. Previous work has shown that the tone mapping problem can formulated as that of optimizing a two-term cost function where adjusting the relative weightings of the two terms allows users to interactively control the appearance of the output image. However, only heuristic solutions to the tone mapping objective function have been found in past research. The main contribution of this paper is the re-formulation of the objective function to allow the introduction of a closed-form solution to the two-term tone mapping objective function. The new solution has simplified previous heuristic solutions and made this approach mathematically more elegant, computationally faster and practically easier to implement. 1.