MT9V124
CONFIDENTIAL AND PROPRIETARY
NOT FOR PUBLIC RELEASE
www.onsemi.com
13
Figure 16. +225 Hue
REG= 0xA00F, 0x16 // CAM_HUE_ANGLE
Auto Exposure
The AE algorithm performs automatic adjustments of the
image brightness by controlling exposure time, and analog
gains of the sensor core as well as digital gains applied to the
image.
The AE algorithm analyzes image statistics collected by
the exposure measurement engine, and then programs the
sensor core and color pipeline to achieve the desired
exposure. AE uses 4 × 4 exposure statistics windows, which
can be scaled in size to cover any portion of the image.
The MT9V124 uses Average Brightness Tracking
(Average Y), which uses a constant average tracking
algorithm where a target brightness value is compared to a
current brightness value, and the gain and integration time
are adjusted accordingly to meet the target requirement. The
MT9V124 also has a weighted AE algorithm that allows the
sensor to be configured to respond to scene illuminance
based on each of the weights in the windows.
The auto exposure can be configured to respond to scene
illuminance based on certain criteria by adjusting gains and
integration time based on scene brightness.
Auto White Balance
The MT9V124 has a built-in AWB algorithm designed to
compensate for the effects of changing spectra of the scene
illumination on the quality of the color rendition. The
algorithm consists of two major parts: a measurement
engine performing statistical analysis of the image and a
module performing the selection of the optimal color
correction matrix, digital, and sensor core analog gains.
While default settings of these algorithms are adequate in
most situations, the user can reprogram base color correction
matrices and place limits on color channel gains.
The AWB algorithm estimates the dominant color
temperature of a light source in a scene and adjusts the B/G,
R/G gain ratios accordingly to produce an image for sRGB
display in which grey and white surfaces are reproduced
faithfully. This usually means that R,G,B are roughly equal
for these surfaces hence the word “balance”.
The AWB algorithm uses statistics collected from the last
frame to calculate the required B/G and R/G ratios and set
the blue and red analog sensor gains and digital SOC gains
to reproduce the most accurate grey and white surfaces
Flicker Detection and Avoidance
Flicker occurs when the integration time is not an integer
multiple of the period of the light intensity. The automatic
flicker detection module does not compensate for the flicker,
but rather avoids it by detecting the flicker frequency and
adjusting the integration time. For integration times below
the light intensity period (10 ms for 50 Hz environment,
8.33 ms for 60 Hz environment), flicker cannot be avoided.
While this fast flickering is marginally detectable by the
human eye, it is very noticeable in digital images because the
flicker period of the light source is very close to the range of
digital images’ exposure times.
Many CMOS sensors use a “rolling shutter” readout
mechanism that greatly improves sensor data readout times.
This allows pixel data to be read out much sooner than other
methods that wait until the entire exposure is complete
before reading out the first pixel data. The rolling shutter
mechanism exposes a range of pixel rows at a time. This
range of exposed pixels starts at the top of the image and then
“rolls” down to the bottom during the exposure period of the
frame. As each pixel row completes its exposure, it is ready
to be read out. If the light source oscillates (flickers) during
this rolling shutter exposure period, the image appears to
have alternating light and dark horizontal bands.
If the sensor uses the traditional snapshot readout
mechanism, in which all pixels are exposed at the same time
and then the pixel data is read out, then the image may appear
overexposed or underexposed due to light fluctuations from
