FLIR LWIR Installation and operating instructions

FLIR Camera Adjustments
LWIR Video Camera
Application Note
FLIR Commercial Systems
70 Castilian Drive
Goleta, CA 93117
Phone: [phone]
www.flir.com
Document Number: 102-PS242-100-01
Version: 110
Issue Date: June 2014

FLIR Camera Adjustments
Table ofContents
FLIR Camera Adjustments...........................................................................................................................1
LWIR Video Camera....................................................................................................................................1
Application Note...........................................................................................................................................1
Document Number: 102-PS242-100-01 .......................................................................................................1
1.0 Document..........................................................................................................................................4
1.1 Revision History ...........................................................................................................................4
1.2 Scope.............................................................................................................................................4
2.0 Automatic AGC Parameters..............................................................................................................5
2.1 Introduction to Histograms ...........................................................................................................6
2.2 Linear Histogram..........................................................................................................................7
2.3 Plateau Histogram Equalization....................................................................................................8
2.4 Information-based and Information-based equalization .............................................................20
2.5 Legacy AGC modes....................................................................................................................21
2.5 Digital Data Enhancement (DDE) ..............................................................................................22
3.0 LUT Palettes and Polarity...............................................................................................................24
4.0 FFC Warning Indicator...................................................................................................................27
Table ofFigures
Figure 1: 14-bit Histogram............................................................................................................................6
Figure 2: Image of scene...............................................................................................................................7
Figure 3: Linear AGC, ITT Mean: 127, Max Gain: 8...................................................................................7
Figure 4: Illustration of the Linear-Histogram Mapping Function.............................................................8
Figure 5: Plateau: 150, ITT Mean: 127, Max Gain: 8...................................................................................9
Figure 6: Image Transform Table for Linear and Plateau algorithms.........................................................10
Figure 7: Plateau: 250, ITT: 110, Max Gain: 8...........................................................................................11
Figure 8: Plateau: 250, ITT: 150, Max Gain: 8...........................................................................................11
Figure 9: Low contrast scene in 14-bit space..............................................................................................12
Figure 10: Plateau: 250, ITT: 127, Max Gain: 8.........................................................................................12
Figure 11: Low contrast Scene: default settings.........................................................................................12
Figure 12: Plateau: 250, ITT: 127, Max Gain: 25.......................................................................................13
Figure 13: Low contrast scene: high gain...................................................................................................13
Figure 14: Plateau: 250, ITT: 127, Max Gain: 50.......................................................................................13
Figure 15: Low Contrast Scene: very high gain .........................................................................................13
Figure 16: Illustration of Plateau Value....................................................................................................14
Figure 17: Illustration of Maximum Gain in a Bland Image ....................................................................15
Figure 18: Illustration of ITT Midpoint....................................................................................................16

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Figure 19: Illustration of Active Contrast Enhancement (ACE).................................................................17
Figure 20: Illustration of Smart Scene Optimization (SSO).......................................................................18
Figure 21: Illustration of ROI...................................................................................................................19
Figure 22: Illustration of the difference between Plateau Equalization, Information-based, and
Information-based Equalization algorithms................................................................................................21
Figure 23: Illustration of Information Threshold........................................................................................21
Figure 24: Illustration of Noise Suppression with DDE...........................................................................23
Figure 25: Illustration of Detail Enhancement with DDE ........................................................................23
Figure 26: White Hot..................................................................................................................................24
Figure 27: Black Hot...................................................................................................................................24
Figure 28: Look-Up Table Options (Without Isotherms)...........................................................................25
Figure 20: Isotherm LUT Scale Example...................................................................................................26
Figure 21: Look-Up Table Options (with Isotherms).................................................................................27
Figure 29: FFC Warning.............................................................................................................................28

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1.0 Document
1.1 Revision History
Version
Date
Comments
100
10/25/2011
Initial Release
110
6/20/2014
Updates for the Tau 2.7 and Quark 2 release
1.2 Scope
This note is intended to provide a better understanding of FLIR image processing algorithms. Once these
are well understood by the user, the camera can be optimized to give the best possible image for a given
scenario. This document applies to the FLIR Quark, Quark 2, Tau, Tau 2 and Neutrino cameras. These
cores can be found in most FLIR Commercial Systems products.
The FLIR website will have the newest version of this document as well as offer access to many other
supplemental resources: http://www.flir.com/cvs/cores/resources/
Here is a sample of some of the resources that can be found:
Document Title
Document
Number
Description
Tau Quick Start Guide
102-PS242-01
Quick Start Guide for first-time use
Quark Quick Start Guide
102-PS241-01
Quick Start Guide for first-time use
FLIR Camera Controller GUI
User’s Guide
102-PS242-02
Detailed Descriptions for functions and adjustments
for FLIR cameras using the FLIR Camera Controller
GUI
Tau 2 Product Specification
102-PS242-40
Product specification and feature description
Quark Product Specification
102-PS241-40
Product specification and feature description
Tau 2 Electrical IDD
102-PS242-41
Written for Electrical Engineers to have all necessary
information to interface to a Tau 2 camera
Quark Electrical IDD
102-PS241-41
Written for Electrical Engineers to have all necessary
information to interface to a Tau 2 camera
Tau 2/Quark Software IDD
102-PS242-43
Written for Software Engineers to have all necessary
information for serial control of Tau 2 and Quark
Assorted Mechanical
Drawings and Models
Various
There are drawings and 3D models for various
camera configurations for mechanical integration
Application Notes
Various
Written for Systems Engineers and general users of
advanced features such as Gain Calibration,
Supplemental FFC Calibration, NVFFC Calibration,
Camera Link, On-Screen Symbology, AGC/DDE
explanation, Camera Mounting, Spectral Response,
Optical Interface for lens design, and others.
There is also a large amount of information in the Frequently Asked Questions (FAQ) section on the
FLIR website: http://www.flir.com/cvs/cores/knowledgebase/. Additionally, a FLIR Applications
Engineer can be contacted at 888.747.FLIR (888.747.3547).

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2.0Automatic AGC Parameters
The first thing to understand is that the detector data is directly streamed from the sensor as 14-bit values
for each pixel in the array. The analog image is displayed using 8-bit values and almost all commercial
displays are 8-bit devices. In other words the video is displayed on a 0-255 scale rather than the full 0-
16384 resolution of the sensor. This means that there must be some compression to get the data into a
format that can be displayed. Throughout this note, there are histograms that are represented in Signal vs.
Number of Pixels. A histogram is a sorting of pixel values into intensity “bins”. What this means is the
bit value (which increases as pixels get brighter) is on the x-axis and the number of pixels in the image
that have that bit value is on the y-axis. This is a way of plotting image data in order to illustrate which
are the most significant intensity values. The algorithms attempt to compress the data in a meaningful
way that preserves as much of the image content as possible.
The Tau 2 core provides multiple AGC algorithms used to transform 14-bit data to 8-bit. These options
include the following, with associated parameters shown below each algorithm:
Plateau equalization
oPlateau value
oMaximum gain
oITT midpoint
oACE threshold
oSSO value
oTail rejection
oRegion of Interest (ROI)
oIIR filter
Information-based and Information-based equalization
oInformation-based Threshold
Linear histogram
oITT midpoint
oROI
oIIR filter
Manual
oBrightness
oContrast
oIIR filter

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Auto-bright
oBrightness
oContrast
oIIR filter
Once-bright
oBrightness bias
oContrast
oIIR filter
Note: FLIR highly recommends that each customer optimize AGC settings for each particular
application. “Preferred” AGC settings are highly subjective and vary considerably depending upon
scene content and user preferences. Generally speaking, FLIR recommends the plateau equalization
algorithm, but there are scenarios where each of the other algorithms may be better suited. The FLIR
GUI provides auto presets that can be used to tune AGC to the specific scene.
2.1 Introduction to Histograms
The following histogram is the 14-bit data taken from a Tau 320 with a cold water bottle, a mid-
temperature wall, and a hot coffee mug in the scene. These three objects can be seen in the data histogram
as three separate peaks. The lowest bit values, which are farthest to the left in the histogram, are the
coldest pixels in the scene. The values in 14-bit space range from 0 to 16384.
Figure 1: 14-bit Histogram
The following image is associated with this histogram. You can see the cold, black water bottle, the grey
background, and the hot, white coffee mug. You can also see that the water bottle is fairly uniform and
1
2
3

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has a narrow spike whereas the mug has different temperatures in the handle and above the coffee line.
For this reason, the data is more spread in the histogram at point 3.
Figure 2: Image of scene
2.2 Linear Histogram
The first and simplest method to translate the data into 8-bit space is using a linear algorithm. Although
this algorithm is not typically used, it will help illustrate the concept of using a transfer funciton to map
from one space to another. A typical linear intensity transfer table will map the middle 90% of the
histogram to 8 bit space. The bottom and top 5% are discarded. This algorithm finds the interesting
portion of the data and crops above and below it. The following histogram is a representation of the same
scene in 8-bit space. Notice the three peaks from the 14-bit data represented in 8-bit space and that the
values on the x-axis now range from 0-255 rather than 0-16384.
Figure 3: Linear AGC, ITT Mean: 127, Max Gain: 8
1
2
3
1
2
3

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The linear histogram algorithm performs a linear transformation from 14-bit to 8-bit of the form:
8biti= m * 14biti+ b
The slope of the transformation is computed automatically based on the ROI histogram:
m = 255 / (14bit_(100 –Tail Rejection)% - 14bit_(Tail Rejection)%),
where 14bit_(Tail Rejection)% is the 14-bit value corresponding to the user selectable tail rejection
percentage point on the cumulative ROI histogram and 14bit_(100 –Tail Rejection)% is the value
corresponding to the difference between 100% and the user selectable tail rejection percentage point in
Tau 2.7 and Quark 2.0.
The offset is then computed as
b = ITT midpoint - avg(14bit_(100 –Tail Rejection)%, 14bit_(Tail Rejection)%),* m
In other words, the algorithm attempts to map the midway point between the top and bottom tail rejection
points on the cumulative histogram to the specified ITT midpoint, as shown in Figure 4 for the case in
which the tail rejection parameter selected is 5%. The 8-bit values resulting from the above equations are
clipped to a minimum value of 0 and a maximum value of 255.
(a) ITT Midpoint = 128
(b) ITT Midpoint = 96
Figure 4: Illustration of the Linear-Histogram Mapping Function
2.3 Plateau Histogram Equalization
The Plateau Histogram Equalization algorithm seeks to maximize the dynamic range available for the
content of the scene. It does this using a transfer function that is based on the number of pixels that are in
each bin and allocating more 8-bit range for that bin. The Plateau value is the pixels/bin limit when the
transfer function is maximized. When this number is small, the Automatic AGC will approach a linear
algorithm that preserves a linear mapping between the 14-bit and 8-bit data. The goal of the Automatic
algorithm is to try and make each of the 255 bins have the same number of pixels in it, which should give
the best contrast for the given scene. When the plateau value is higher, the algorithm is more able to
14bit_5% 14bit_95%
Avg(14bit_5%, 14bit_95%)
specified
ITT Midpoint
14bit_5% 14bit_95%
Avg(14bit_5%, 14bit_95%)
specified
ITT Midpoint

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redistribute the data to achieve this goal. This prevents wasted levels of grey on regions that have no
scene content and can visually be seen in the histograms by noticing that peaks are much smoother and
the data is spread much more evenly.
Figure 5: Plateau: 150, ITT Mean: 127, Max Gain: 8
Compare to Linear Histogram in Figure 3
The following plot shows the Image Transform Table for both Linear and Plateau Histogram
Equalization. The 14-bit value on the x-axis will map to the 8-bit value on the y-axis where the
conversion is plotted. In 14-bit regions with low contrast, the curve is much flatter and there are not as
many 8-bit values consumed. In high detail regions, the curve is steep and more 8-bit values are used.

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Figure 6: Image Transform Table for Linear and Plateau algorithms
In many applications, there are objects with different temperatures that all need to have contrast. In this
case, the plateau value can be increased from the default setting of 150 to 250 or 300, depending on the
content of the scene. This will provide the algorithm with more flexibility to generate an image with high
contrast for both foreground and background targets. In applications where there is a large object of a
small temperature range and the background is not important, lowering the plateau value to 100 or 50 will
dedicate more contrast to the foreground object and the background will have less. This can also be done
when an AGC ROI is used to discard scene content that is not important.
It is important to note that higher plateau value distorts the correlation between physical temperature of
the scene and level of grey in the image, which is preserved in a linear histogram.
The ITT Mean sets the midpoint of the Image Transform Table and is applied after the histogram
equalization on the 8-bit data. This can be thought of as an offset that shifts the entire distribution to the
left or the right and directly affects the brightness of the image. It is important to notice that when the data
is shifted, it is cropped and it is not stretched to fill the 8-bit range. This means that you are losing data by
shifting, but the data on the top and bottom may not be the most important. Increasing the Plateau Value,
as seen above, typically raises the perceived brightness of the scene. This could be counteracted by
lowering the ITT Mean from the default of 127 to 120.
Linear 14 to 8 bit
Histogram Equalized
Lots of Contrast
Low Contrast
conversion
Low Contrast

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It is also important to note that changes in levels of grey are more perceptible to a human observer at
lower levels of illumination. This is because a change from 5 to 10 counts is 100% and a change from 245
to 250 is about 2%. By lowering the ITT Mean, you can shift the luminance to a lower value so that a
human observer will be more capable of seeing small changes in the scene. If you are using analytics, the
performance will not likely be improved by this change and the default setting of 127 is recommended.
Figure 7: Plateau: 250, ITT: 110, Max Gain: 8
Figure 8: Plateau: 250, ITT: 150, Max Gain: 8
The final AGC parameter is the Max Gain. To demonstrate this, it will be better to look at a scene with
very low contrast. The scene used was a uniform wall with an empty paper cup that was slightly warmer
than the wall. Notice the very narrow spike in the 14-bit data and the very small bump just to the right of
the spike.

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Figure 9: Low contrast scene in 14-bit space.
The large spike from the wall is the same value as in initial histogram.
The following histogram shows the 8-bit data with the default Max Gain setting of 8. Notice that
there is a large amount of unused levels of grey on the left and right of the signal. The maximum gain
setting sets the upper limit of gain that the algorithm can use as it attempts to stretch the data to fill the
full 8-bit range. If the scene has a high level of contrast, it will use much less gain than the maximum gain
setting. In typical applications, this value can be increased from the default of 8 to 12 or 15. Analytics that
are not affected by spatial noise might tolerate higher values around 25 or 30. Values below are used for
demonstration and do not represent typical settings.
Figure 10: Plateau: 250, ITT: 127, Max Gain: 8
Figure 11: Low contrast Scene: default settings
His
tog

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Figure 12: Plateau: 250, ITT: 127, Max Gain: 25
Figure 13: Low contrast scene: high gain
Figure 14: Plateau: 250, ITT: 127, Max Gain: 50
Figure 15: Low Contrast Scene: very high gain
The plateau equalization algorithm performs a non-linear transformation from 14-bit to 8-bit based on
image histogram. It is a variant of classic histogram equalization, an algorithm that maps 14-bit to 8-bit
using the cumulative histogram of the 14-bit image as the mapping function. In classic histogram
equalization, an image comprised of 60% sky will devote 60% of the available 8-bit shades to the sky,
leaving only 40% for the remainder of the image. Plateau equalization limits the maximum number of
gray shades devoted to any particular portion of the scene by clipping the histogram (via the plateau
value) and limiting the maximum slope of the mapping function (via the maximum gain value). It also
provides an ITT midpoint value that allows mean brightness of the 8-bit image to be specified. The Tau
2.7 release includes the ability to allot a linear portion to the histogram (via Smart Scene Optimization),
include an irradiance dependent contrast adjustment (via Active Contrast Enhancement), and specify
outlier rejection (via Tail Rejection). The description below provides explanations of each of these
parameters.
Lo
ts
of
co
nt
ra
st

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Plateau value. When plateau value is set high, the algorithm approaches the behavior of classic histogram
equalization –gray shades are distributed proportionally to the cumulative histogram, and more gray
shades will be devoted to large areas of similar temperature in a given scene. On the other hand, when
plateau value is set low, the algorithm behaves more like a linear AGC algorithm –there is little
“compression” in the resulting 8-bit histogram. The figures below illustrate both of the cases.
(a) Plateau Value = 1000
(b) Plateau Value = 10
(c) 8bit Histogram for Plateau Value = 1000
(d) 8-bit Histogram for Plateau Value = 10
Figure 16: Illustration of Plateau Value
(Notice details in the sidewalk in the left image whereas more gray shades are available for the
pedestrians in the right image.)

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Maximum Gain. For scenes with high dynamic range (that is, wide 14-bit histogram), the maximum gain
parameter has little effect. For a very bland scene, on the other hand, it can significantly affect the
contrast of the resulting image. Figure 17 provides an example.
(a) Maximum Gain = 6
(b) Maximum Gain = 24
(c) 8bit Histogram for Max. Gain = 6
(d) 8bit Histogram for Max. Gain = 24
Figure 17: Illustration of Maximum Gain in a Bland Image
(Notice more details but also greater fixed-pattern noise in the right image.)

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ITT Midpoint. The ITT Midpoint can be used to shift the 8-bit histogram darker or brighter. The
nominal value is 128. A lower value causes a darker image, as shown in Figure 18. A darker image can
help improve the perceived contrast, but it is important to note that more of the displayed image may be
railed (8bit value = 0 or 255) by moving the midpoint away from 128. This can be seen in the histogram
of Figure 18.
(a) ITT Midpoint = 127
(b) ITT Midpoint = 96
(c) 8bit Histogram for ITT Midpoint = 127
(d) 8bit Histogram for ITT Midpoint = 96
Figure 18: Illustration of ITT Midpoint
(Notice image on the right is darker. Notice in the histogram on the right that far more pixels have
a value of 0 and that no pixels have a value between 224 and 255.)

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ACE Threshold. The Active Contrast Enhancement (ACE) feature provides a contrast adjustment
dependent on the relative scene temperature. ACE thresholds greater than 0 impart more contrast to hotter
scene content and decrease contrast for colder scene content (e.g. sky or ocean). ACE threshold less than
0 do the opposite by decreasing contrast for hotter scene content and leaving more of the gray-scale
shades to represent the colder scene content. Figure 19 illustrates the effects of ACE. FLIR recommends a
conservative setting of 3 for generic use-case scenarios.
(a) ACE threshold = -4 (b) ACE threshold = 0 (neutral) (c) ACE threshold = 8
Figure 19: Illustration of Active Contrast Enhancement (ACE)
SSO Value. The Smart Scene Optimization (SSO) value defines the percentage of the histogram that will
be allotted a linear mapping. Enabling this feature facilitates the avoidance of irradiance level
compression, which is specifically important for bi-modal scenes. With SSO enabled, the radiometric
aspects of an image are better preserved (i.e. the difference in gray shades between two objects is more
representative of the difference in temperature). While radiometry is better preserved with this feature, the
compromise is the optimization in local contrast. Figure 20 illustrates the effects of SSO. In the left
image, SSO is disabled and the hot object and person get mapped to two shades of gray next to one
another causing a “washed out” effect and making it appear as though the person and fire are similar in
temperature. In the right image, SSO is enabled, and the hot object and person are decompressed with
gray shades separating them.

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(a) SSO = 0% (disabled) (b) SSO = 30%
(c) 8bit Histogram of SSO = 0% (disabled) (d) 8bit Histogram of SSO = 30%
Figure 20: Illustration of Smart Scene Optimization (SSO)
Tail Rejection. The tail rejection parameter defines the percentage of the total number of pixels in the
array that will be excluded prior to histogram equalization. The user-selected percentage of pixels will be
removed from both the bottom and top of the 14-bit histogram prior to AGC. This feature is useful for
excluding outliers and the most extreme portions of the scene that may be of less interest. FLIR
recommends tail rejection settings less than 1% to avoid the exclusion of important scene content.
Region of Interest (ROI). In some situations, it is desirable to have the AGC algorithm ignore a portion
of the scene when collecting the histogram. For example, if the Tau 2 core is rigidly mounted such that
the sky will always appear in the upper portion of the image, it may be desirable to leave that portion of
the scene out of the histogram so that the AGC can better optimize the display of the remainder of the
image. This is illustrated in Figure 21. Similarly for a hand-held application, it may be desirable to
optimize the display of the central portion of the image. For those applications, it is possible to specify a
region of interest (ROI) beyond which data is ignored when collecting the image histogram. Any scene
content located outside of the ROI will therefore not affect the AGC algorithm. (Note: this does not mean
the portion outside of the ROI is not displayed, just that the portion outside does not factor into the
optimization of the image.)

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For Tau 2.0, separate ROI are automatically applied for un-zoom, 2X, 4X, and 8X zoom. Coordinates for
the ROI are as follows:
Top / Bottom: 0 = center of the display, negative values are above center, positive values are below center,
units are in pixels
Left / Right: 0 = center of display, negative values are left of center, positive values are right of center,
units are in pixels
For Tau 2.1 and later, a single ROI is specified regardless of zoom (see product specification).
Coordinates for the ROI are as follows:
Top / Bottom: 0 = center of the window, negative values are above center, positive values are below
center, units are percentage of zoom window size (-512 = -50%, +512 = + 50%).
Left / Right: 0 = center of display, negative values are left of center, positive values are right of center,
units are percentage of zoom window size (-512 = -50%, +512 = + 50%).
(b) ROI = Full Image
(b) Sky excluded from ROI
Figure 21: Illustration of ROI
(Notice the image on the right has more contrast in the portion of the image below the sky.)

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AGC Filter. The AGC filter is an IIR filter used to adjust how quickly the AGC algorithm reacts to a
change in scene or parameter value. The filter is of the form
n' = n*  + n'-1 * (256-)/256
where:
n' = actual filtered output value for the current frame
n= unfiltered output value for the current frame
n'-1 = actual filtered output value for the previous frame
= filter coefficient, user-selectable from 0 to 255
If the AGC filter value is set to a low value, then if a hot object enters the field of view, the AGC will
adjust more slowly to the hot object, resulting in a more gradual transition. In some applications, this can
be more pleasing than a sudden change to background brightness. For the Tau 2.7 release, a filter
coefficient of 255 is a special case for immediate updates, a value of 1 provides the most filtering or
slowest refresh rate, and a value of 0 indicates no further updates to AGC. For previous releases of Tau 2,
a filter coefficient of 0 was a special case for immediate updates, a value of 1 was the most filtering or
slowest refresh rate, and the case for no AGC updates was unavailable.
2.4 Information-based and Information-based equalization
The Tau 2.7 release and subsequent releases include the Information-based algorithms which reserve
more shades of gray for areas with more information or scene content by assigning areas with less
information or scene content lesser gray shades. By assigning lesser gray shades to areas with less
information (e.g. sky, sea, roads) the fixed pattern noise is reduced in these areas also allowing for more
detail to be given to the more interesting portions of the image. Both Information-based algorithms
undergo the plateau equalization process described in the previous section, and therefore all parameters
described also apply.
The differences between the Information-based and Information-based Equalization algorithms are
noteworthy as both have advantages. The Information-based algorithm completely excludes pixels from
histogram equalization if they are below the information threshold (described later in this section). This is
advantageous in that noise is completely removed from areas of the image determined by the algorithm to
not contain information, but scenarios in which the user is attempting to detect small temperature or
emissivity variations are not ideal for this mode because desired information may be lost depending on
the threshold. The Information-based Equalization algorithm includes every pixel independent of scene
information in the histogram equalization process, but simply weights each pixel based on the information
threshold. This mode shows more moderate improvements in scenes with large areas void of information,
but the advantage over the Information-based mode is that scene content will never be removed. Figure
22 shows the Plateau Equalization algorithm on the left for reference and the Information-based and
Information-based Equalization algorithms center and right respectively with information threshold set to
40.