For the purpose of a machine learning algorithm I'm tying to automatically scale images of arbitrary bitdepth to unit interval, i.e. I want to map pixels from [0,2x] to [0,1] simply by dividing all pixels by 2x. I want to do this without knowing anything about the pixelvalue distribution or statistics inside the image. The only thing that I know is the metadata. To open images I use gdal in python via

data = gdal.Open(path)

The images are given in .tif or .img format.


Typically 12bit images will be saved in a 16bit format, where pixel values above 4095 won't occure. Nevertheless the meta tag bitdepth will be 16bit.


Is there any valid way to check whether an image is a true 16bit image or a 12bit image inside a 16bit image, without calculating the statistics? Is there any meta tag that serves this information?

2 Answers 2


There is a meta tag but it isn't in the default domain:

data = gdal.Open(path)
band = data.GetRasterBand(1) 
# {'NBITS': '12'}

For the full list of metadata domains use


At least when the GeoTIFF image is created with GDAL you can check the NBITS value with gdalinfo.

Create a test image

gdal_translate -of gtiff -ot uint16 -co nbits=12 base.tif base12.tif

Have a look at the metadata

gdalinfo base12.tif
Driver: GTiff/GeoTIFF
Files: base12.tif
Size is 10000, 10000
Coordinate System is `'
Image Structure Metadata:
Corner Coordinates:
Upper Left  (    0.0,    0.0)
Lower Left  (    0.0,10000.0)
Upper Right (10000.0,    0.0)
Lower Right (10000.0,10000.0)
Center      ( 5000.0, 5000.0)
Band 1 Block=10000x1 Type=UInt16, ColorInterp=Red
  Image Structure Metadata:
Band 2 Block=10000x1 Type=UInt16, ColorInterp=Green
  Image Structure Metadata:
Band 3 Block=10000x1 Type=UInt16, ColorInterp=Blue
  Image Structure Metadata:
  • Thank you very much for your answer. Unfortunately I don't have the control about the data creation, but I see your point. Somehow the information needs to be added manually somewhere to the meta tags i.e. the workflow of early data processing needs to be adapted.
    – pafi
    Commented Jul 1, 2019 at 13:37

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