Spark Colour Calibration
If you encounter an issue with the Videri Spark series having colour discrepencies:
Firmware Update Solution
The majority of colour matching issues can be resolved with the latest firmware update (vle1.2.8). This firmware should automatically download when the Canvas is initially setup so please allow the Canvases time to fully install the latest firmware before testing/assessment. You can check the currently installed version on each Canvas by navigating to Portal3, select the Canvas from your list, select "Advanced Settings" then select the "Firmware & APKs" tab to view the "Firmware build". You can view the latest versions here: Canvas Software and Firmware Versions
Please note that all future batches of Canvases will come with VLE 1.2.8 (or greater) pre-installed and will undergo color matching at the factory
Additional Support for Remaining Cases
For the remaining cases where the firmware is updated but the colour calibration still does not meet client expectations, it is also possible to perform some manual adjustments via the Command Terminal interface in Portal3.
Before proceeding, browse to the Display Settings of the Canvas, reset the Colour Settings to default values and ensure the Brightness and Saturation values are set to 50%.
Then, open the Command Terminal and execute the following commands in order:
Reset Colour Settings:
su_shell_cmd:=am startservice -n com.videri.FactoryService/.FactoryService -a com.videri.FactoryService.RESET_COLOR
Apply Saturation, Hue and Contrast:
su_shell_cmd:=am startservice -n com.videri.FactoryService/.FactoryService -a com.videri.FactoryService.SET_SATURATION --ei saturation 50
su_shell_cmd:=am startservice -n com.videri.FactoryService/.FactoryService -a com.videri.FactoryService.SET_HUE --ei hue 50
su_shell_cmd:=am startservice -n com.videri.FactoryService/.FactoryService -a com.videri.FactoryService.SET_CONTRAST --ei contrast 50
Set Colour Temperature:
set_color_temperature:=6500
Once that is done, apply the appropriate LUT table command depending on the Spark Canvas model you have
Spark3:
Spark4:
Spark5:
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