Color Night Vision specific performance characterization
Jonathan A. Walkenstein*a, Edward H. Currie*a, Daniel J. Hosek **b, William B. Pardo***c, Marco Monti***c, J. Brian Gillespie***d,aCANVS Corporation, bU.S. Army CECOM Night Vision Electronic Sensors Directorate, cThe Nonlinear Dynamics Laboratory Chromatic Diagnostic Facility University of Miami Physics Department, dBall Aerospace & Technologies Corp.
The Nonlinear Dynamics Laboratory
Night Vision Electronic
Ball Aerospace &
The “night vision” community has developed and refined a series of test and measurement routines for characterization of "green monochrome" tube and system performance. Although these tools could be directly applied to Color Night Vision Systems, a detailed re-examination of these techniques in the context of “Color” provides a more meaningful metric. The creation of a color contrast measure is proposed as the first step in the development of Color Night Vision specific performance characterization.
Color is that characteristic of a visible object or light source by which an observer (human) may distinguish differences between two structure-free fields of the same size and shape, such as may be caused by differences in the spectral composition of the light concerned in the observation.1[ref 1, p 723]. In other words, color is that perception by which we can tell two objects apart, when they have otherwise similar attributes of shape, size, texture, etc.
It follows by deduction that, given the case of two structure-free fields of the same size and shape with similar attributes, it would be more difficult or in fact impossible to differentiate between these two fields in a monochrome representation.
1 G. Wyszecki and W.S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae 2nd Ed., Wiley, New York, 1982.
Fig. 1: A pseudoisochromatic plate from Ishihara\'s test on the left, and the same plate “de-saturated”.
Figure 1 dramatically illustrates the ability to positively identify the target (in this case, the number 5 from a colorblindness test plate) in the color case, and a total inability to extract the necessary information in the monochromatic representation of the same scene.
Color Space Models are to the visual researcher as coordinate systems are to the mathematician. In mathematics, specific tasks tend to lend themselves to a particular coordinate system. By converting to a context-specific coordinate system, a complex problem is reduced to a simple and intuitively solved one. In exploring various ideas related to color content in imagery, the tools available to quantify and manipulate information content in imagery lend themselves to specific Color Space models. As in mathematics, as long as the conversion between the various systems is handled with diligence, the availability of numerous Color Space models opens up a wider variety of basic tools to apply to the tasks at hand. Below is a cursory look at RGB, HSV and CIE color spaces.
RGB space is the color space most naturally associated with computers and video monitors. Colors are represented by the additive combinations of Red, Green and Blue light. Values are represented as ranging from 0 to 1, or 0 to 255. 0 to 255 represents 256 intensity values for each channel, this is also know as eight bit per channel (28 = 256). Three eight bit channels combine to produce (in theory) twenty four bit color (224 = 16,777,216). This is commonly referred to as “true color”.
The family of colors that can be produced in RGB space is commonly represented by the RGB Color Cube. As such, the grayscale lies along the line running from the black corner to the white corner.
Fig. 3: The HSV color space, HSV equivalents for the corners of the RGB Color cube, and the V = 1 plane.
Colors in the HSV color space aredefined by Hue, Saturation, and Value. Hue is specified as an angular measure belonging to the set [ 0:360 ], Saturation ranges from [ 0:255 ], with 0 saturation being white at Value = 255, and 0 saturation being black at Value = 0. The grayscale lies on the major axis of the cone.
Fig. 4: The CIEcolor space, CIE equivalents for the corners of the RGB Color cube, and the CIE diagram showing the available colors as restricted by the RGB phosphors on a common computer display. It should be noted that there is no unique one-to-one mapping between observed wavelengths and RGB values.
While RGB and HSV color spaces are of general usefulness when dealing with digitized imagery on a computer, the CIE color space was designed for human-in-the-loop interpretation of color content as observed in the real world.
Color Contrast (C12 = the contrast between two colors C1 and C2) is defined as a quantity that has magnitude and direction and that is represented by a directed line segment whose length represents the magnitude (of the color contrast) and whose orientation in space represents the direction. Color Contrast can be calculated for various Color Spaces.
The metric of XYZ color space is defined as C12 = [(X1-X2)2 + (Y1-Y2)2 + (Z1-Z2)2]1/2
C12X = [(X1-X2)2]1/2 C12Y = [(Y1-Y2)2]1/2 C12Z= [(Z1-Z2)2]1/2
C12 space is a cube with sides of length equal to one. Coordinates in C12 space range from [0,0,0] to [1,1,1].
The minimum value of C12 occurs when C1 is equal to C2 (the same color, X1=X2 , Y1=Y2, Z1=Z2).
By definition for C1 = C2 , it follows that C12 = [(X1-X2)2 + (Y1-Y2)2 + (Z1-Z2)2]1/2 = 0
Note that the minimum C12 can occur at any location within C12 space.
Conversely there are four instances of the maximum value for C12.
These four instances occur at opposite corners of the C12 space cube. In the RGB color space, maximum values of C12 occur at the following C1, C2 pairs:
Fig. 5: Graphic illustration of the effect of direction, C12 vs. C21.
The process for calculating C12 values is demonstrated on a pseudoisochromatic plate from Ishihara\'s color blindness test. Although pixel-to-pixel C12 values can be calculated, it is more meaningful to calculate area-to-area C12 values. For this particular image, an area of twenty by twenty pixels was selected. This block size was set based on the feature size. The image was converted into the Tagged Image File Format (TIFF) to facilitate Matlab manipulation of the image. Sample data and output is presented below.
Clay M. Thompson, Loren Shure Image Processing Toolbox Users Guide with Matlab, August 1993
The 20 by 20 pixel areas were extracted as matrices containing twenty rows, twenty columns, and three layers (R,G, and B values). The mean of this 20 x 20 x 3 matrix was calculated and represents the “mean color” (C1RGB and C2RGB) of the 20 by 20 pixel subsections of the image. These areas were then decomposed into their RGB and HSV components.
Fig. 8: C12 RGBcolor and C12 HSVcolor visualization.
Fig. 9: C1 and C2 represent the mean colors of areas C1 and C2.
Fig. 11: C12RGB gray and C12HSVgray visualization.
images: RGBcolor , HSVcolor , RGBgray , and HSVgray .
An examination of the decomposed images suggests that the information content in the S of HSV color representation of the image contains a disproportionate amount of the information necessary for target recognition. This is also reflected by examination of the C12 data.
Fig. 13: The magnitude of the vectors C12 and C21 are equal. Direction of the vectors
manifests itself visually when the colors are viewed in a field surround representation.
C12 Calculations for Color Night Vision vs. Green Night Vision imagery.
Fig. 14: Color Night Vision vs. GEN-III OMNI-IV “green” Night Vision.
Figure 14was acquired from live split screen video shot at NVESD**b in February of 1998. The left image is from an early prototype Color Night Vision imager and the right image is from a GEN-III OMNI-IV image-intensifier-based camera. Lighting was typical of a mixed, urban night environment. The target is a white car showing three groups of twenty rounds each of different optical markers applied with a .68 caliber gun at typical Close Quarters Combat distances.
Fig. 15: C1 and C2 represent the mean colors of areas C1 and C2.
Fig. 17: Side by side comparison of C12 RGBcolor and C12 HSVcolor values.
Fig. 18: C1 and C2 represent the mean colors of areas C1 and C2.
Fig. 20: Side by side comparison of C12 RGBgreen and C12 HSVgreen values.
Table of calculated C12 values. Note that C12 RGBcolor is ~ 4.6 times larger than C12 RGBgreen.
Fig. 21: RGB decomposition for RGBcolor and RGBgreen images showing region C1 and C2.
C12 calculations provide a means for objectively measuring color contrast in real images. Additional measures are needed to provide a more comprehensive quantization of information content in color vs. “green” and monochromatic imagery.
Quantization of the decomposed imagery provides a starting point for further analysis of information content in Color vs. “green” imagery (a dramatic illustration of this can be seen in Figure 12 the S of HSVcolor image).
Human factors associated with color vision need to be considered for direct view, and human-in-the-loop color contrast measurements.
A color contrast test fixture needs to be developed (taking into account Human factors of color vision).
CANVS Corporation would like to acknowledge NVESD as an unbiased observer of the evolution of CANVS Color Night Vision systems, technologies, and techniques.
CANVS acknowledges The Nonlinear Dynamics Laboratories Chromatic Diagnostic Facility (University of Miami Physics Department). CANVS looks forward to the continued working relationship with NDL and CDF aimed at developing (and providing) tools and techniques for the scientific community (particularly as it applies to Basic Research across disciplinary boundaries). http://ndl.physics.miami.edu
The Nonlinear Dynamics Laboratory
CANVS would like to acknowledge and thank Ball Aerospace & Technologies Corporation for a most enjoyable and productive working relationship.
Ball Aerospace &