Gabriele Guarnieri

High Dynamic Range Image Display With Halo and Clipping Prevention

The dynamic range of an image is defined as the ratio between the highest and the lowest luminance level. In a high dynamic range (HDR) image, this value exceeds the capabilities of conventional display devices; as a consequence, dedicated visualization techniques are required. In particular, it is possible to process an HDR image in order to reduce its dynamic range without producing a significant change in the visual sensation experienced by the observer. In this paper, we propose a dynamic range reduction algorithm that produces high-quality results with a low computational cost and a limited number of parameters. The algorithm belongs to the category of methods based upon the Retinex theory of vision and was specifically designed in order to prevent the formation of common artifacts, such as halos around the sharp edges and clipping of the highlights, that often affect methods of this kind. After a detailed analysis of the state of the art, we shall describe the method and compare the results and performance with those of two techniques recently proposed in the literature and one commercial software.

Image splitting techniques for a dual layer high dynamic range LCD display

Liquid crystal displays (LCD) are replacing analog film in radiology and permit to reduce diagnosis times. Their typical dynamic range, however, can be too low for some applications, and their poor ability to reproduce low luminance areas represents a critical drawback. The black level of an LCD can be drastically improved by stacking two liquid crystal panels in series. In this way the global transmittance is the pointwise product of the transmittances of the two panels and the theoretical dynamic range is squared. Such a high dynamic range (HDR) display also permits the reproduction of a larger number of gray levels, increasing the bit depth of the device. The two panels, however, are placed at a small distance one from each other due to mechanical constraints, and this introduces a parallax error when the display is observed off-axis. A complex, spatially-adaptive algorithm is therefore necessary to generate the images used to drive the two panels. In this paper, we describe the characteristics of a prototype dual-layer HDR display and discuss the issues involved in the image splitting algorithms. We propose some solutions and analyze their performance, giving a measure of the capabilities and limitations of the device.

Minimum-Error Splitting Algorithm for a Dual Layer LCD Display—Part I: Background and Theory

A dual layer high dynamic range liquid crystal display (LCD) can be built by stacking two panels one on top of the other. In this way, the dynamic range is theoretically squared and the bit depth is also increased. However, in order to minimize the parallax and reconstruction errors, dedicated splitting algorithms are needed to generate the two images which drive the panels. In this paper, we present an algorithm, based on variational techniques, which seeks the joint minimization of both errors. We propose a simplified visible difference metric that exploits some limitations of the human visual system and can be easily incorporated into an optimization algorithm. The image splitting task is formulated as a quadratic programming problem, which can be efficiently solved by means of appropriate numerical methods. Preliminary tests on medical images showed that the algorithm has good performances and appears robust with respect to the parameter adjustment.

Image-splitting techniques for a dual-layer high dynamic range LCD display

Liquid crystal displays (LCDs) are replacing analog film in radiology and reducing diagnosis times. Their typical dynamic range, however, can be too low for some applications, and their poor ability to reproduce low-luminance areas represents a critical drawback. The black level of an LCD can be drastically improved by stacking two liquid crystal panels in series. In this way the global transmittance is the pointwise product of the transmittances of the two panels and the theoretical dynamic range is squared. Such a high dynamic range (HDR) display also permits the reproduction of a larger number of gray levels, increasing the bit depth of the device. The two panels, however, are placed at a small distance from each other due to mechanical constraints, and this introduces a parallax error when the display is observed off-axis. A complex, spatially adaptive algorithm is therefore necessary to generate the images used to drive the two panels. We describe the characteristics of a prototype dual-layer HDR display and discuss the issues involved in the image-splitting algorithms. We propose some solutions and analyze their performance, giving a measure of the capabilities and limitations of the device.

Minimum-Error Splitting Algorithm for a Dual Layer LCD Display—Part II: Implementation and Results

A dual layer liquid crystal display (LCD) is able to achieve a high dynamic range by stacking two liquid crystal panels one on top of the other over an enhanced backlight unit. However, the finite distance between the two panels inevitably introduces a parallax error when the display is observed off-axis, and the dynamic range limitations of the individual panels introduce a reconstruction error near sharp edges in the input image. In Part I, we have formulated the image splitting as a constrained optimization problem in which a joint minimization of the parallax error and the visibility of the reconstruction error is performed.

Nonlinear mapping of the luminance in dual-layer high dynamic range displays

It has long been known that the human visual system (HVS) has a nonlinear response to luminance. This nonlinearity can be quantified using the concept of just noticeable difference (JND), which represents the minimum amplitude of a specified test pattern an average observer can discern from a uniform background. The JND depends on the background luminance following a threshold versus intensity (TVI) function. It is possible to define a curve which maps physical luminances into a perceptually linearized domain. This mapping can be used to optimize a digital encoding, by minimizing the visibility of quantization noise. It is also commonly used in medical applications to display images adapting to the characteristics of the display device. High dynamic range (HDR) displays, which are beginning to appear on the market, can display luminance levels outside the range in which most standard mapping curves are defined. In particular, dual-layer LCD displays are able to extend the gamut of luminance offered by conventional liquid crystals towards the black region; in such areas suitable and HVS-compliant luminance transformations need to be determined. In this paper we propose a method, which is primarily targeted to the extension of the DICOM curve used in medical imaging, but also has a more general application. The method can be modified in order to compensate for the ambient light, which can be significantly greater than the black level of an HDR display and consequently reduce the visibility of the details in dark areas.

Nonlinear mapping for dynamic range compression in digital images

The dynamic range of an image is defined as the ratio between the maximum and minimum luminance value it contains. This value in real images can be several thousands or even millions, whereas the dynamic range of consumer imaging devices rarely exceeds 100; therefore some processing is needed in order to display a high dynamic range image correctly. Global operators map each pixel individually with the same nonlinear function; local operators use spatially-variant functions in order to achieve a higher quality. The lower computational cost of global operators makes them attractive for real-time processing; the nonlinear mapping can however attenuate the image details. In this paper we define an expression which gives a quantitative measure of this artifact, and compare the performance of some commonly used operators. We show that a modified logarithm we propose has a satisfactory performance for a wide class of images, and has a theoretical justification based on some properties of the human visual system. We also introduce a method for the automatic tuning of the parameters of our system, based on the statistics of the input image. We finally compare our method with others proposed in the literature.

61.4: Quantization in Medical Imaging Displays — Initial Observer Results for a High‐Luminance‐Range, Dual‐Layer LCD

observer detection performance with 8- and 16-bit grayscale presentation. Eight readers evaluated 532 image pairs using a two-alternative forced choice experimental design. The image set consisted of synthetic backgrounds generated using the mammography-like cluster lumpy background (CLB) technique with a dual-layer approach with parameter values that have been shown to replicate the correlation structure found in digital mammography. The image pats were reviewed in a display device prototype with one million pixels capable of processing and displaying 16-bit images (up to 65536 luminance values). These image pats were presented either as non-quantized (full range) images in a 16-bit presentation scale, or as quantized, 8-bit images, with a perceptual mapping of gray levels to luminance. The difference in reader performance between reads on quantized image pairs and reads on non-quantized image pairs were derived using fraction of correct decisions. The variance of our measurements was estimated using a multi-reader, multi-case analysis. Average reader performance difference between 16- and 8-bit quantization was 0.065 with an associated standard deviation of 0.048. Our study showed that image quantization is an important factor in visual detection task, that is, a quantization from 16- to 8-bit significantly reduces reader detection performance.

Gray-level mapping in advanced medical displays: Determining the collective and individual visual response of the users

The DICOM Grayscale Standard Display Function (GSDF) is widely used in the medical imaging field to map the image values into luminance emitted by the display. However, the DICOM GSDF is not accurate at very dark luminance levels, and this causes a loss of visibility in the details if an image is viewed on the novel High Dynamic Rance display devices such as the ones based on the Dual Layer LCD technology. In this paper, we describe two experiments we performed in order to measure the eye sensitivity at low luminance levels and thus propose a correction. The first experiment was performed in controlled viewing conditions (dark room and fixed viewing distance) that reproduce as closely as possible those defined in the DICOM specification. The method we chose is a “staircase” procedure and uses the recently proposed “2AFC with denoising” technique, combined with a maximum-likelihood method. The results confirmed that the DICOM model overestimates the eye sensitivity at very low luminance levels. The second experiment was conducted with free viewing distance and with the lights both off and on, in order to simulate more realistic operating conditions. We used the “QUEST” algorithm to conduct the test and to compute the results, using available open-source software. The results show that the free viewing distance can improve the visibility of the details in the dark portions, because the observers tend to move closer to the display. At the same time, the ambient light has a severe impact on the observers performance in the dark portion, but a negligible, and sometimes even slightly positive effect in the bright portions. The final target of this work is to propose a modified Display Function which offers a good performance in the entire luminance range of an HDR display and can be personalized according to the individual characteristics of each observer and to the level of ambient light.

Color enhancement in a high dynamic range environment

We present techniques for the processing of color, high-dynamic luminance images of a type aiming for objectivity and also of a type aiming for aesthetic improvement. In the first case we start with camera raw data, propose a variant white balance, darken very light spots and lighten very dark spots. In the second case we use color spaces of the type hue-saturation-luminance; we propose a hue processing method inspired in the Bezold-Brucke effect as well as a luminance-dependant displacement of color saturation.