KEYWORDS: RGB color model, Cameras, Image processing, Optical filters, Visualization, Sensors, Light sources, Data conversion, High dynamic range imaging, Photography
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.
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.
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.
KEYWORDS: LCDs, Calibration, Prototyping, Medical imaging, High dynamic range imaging, Image resolution, Liquid crystals, Sensors, Image quality standards, Data modeling
We explore the calibration of a high luminance range, dual-layer, liquid crystal display (LCD) prototype. The
operation of the prototype is done by splitting a high luminance resolution image (graylevel > 28) into two 8-bit
depth components and sending these images to the two liquid crystal panels stacked over the backlight module.
By interpolation of a small set of luminance data gathered using a specialized luminance probe, the look-up table
of graylevel pairs of front/back layer LCD and the corresponding luminance values can be generated. To display
images, we fit an extended DICOM model to the interpolated luminance table which is adjustable for graylevel
and luminance depth. A dynamic look up table is generated in which for each luminance there are several graylevel
pair candidates. We show results for one possible calibration strategy involving the pair selection criterion. By
selecting the pair that maximizes back-layer smoothness, the images with arbitrary graylevel and luminance
depth can be then displayed with equal perceptual distance between luminance levels, while minimizing parallax
effects. Other possible strategies that minimize glare and noise are also described. The results can be used for
high luminance range display performance characterization and for the evaluation of its clinical significance.
KEYWORDS: LCDs, High dynamic range imaging, Image filtering, Prototyping, Image processing, Distortion, Transmittance, Linear filtering, Visualization, Algorithm development
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.
KEYWORDS: Image processing, Signal attenuation, RGB color model, Image compression, High dynamic range imaging, Visual system, Eye, Image quality, Visualization, Data conversion
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.
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