Learning the specific feature space of digital images is no big deal. You might explore wavelet transformation and different image formats (BMP, JPEG, JPEG2000). Pixel intensity, color, edge detection, high and low pass filtering, connectivity between objects, pattern recognition... there are a lot of topics.
And then there are disciplines built on top of image processing, like face detection, watermarking, image retrieval/search, editing/transformation (think Photoshop)... and of course video is another can of worms, adding time-series data.
There are strong connections between higher-level image processing and statistical AI and data mining, so you might consider exploring those topics too.
Here's the text we used in my computer vision class, probably the classic in the field:
http://www.amazon.com/Digital-Image-Processing-Rafael-Gonzal...
Learning the specific feature space of digital images is no big deal. You might explore wavelet transformation and different image formats (BMP, JPEG, JPEG2000). Pixel intensity, color, edge detection, high and low pass filtering, connectivity between objects, pattern recognition... there are a lot of topics.
And then there are disciplines built on top of image processing, like face detection, watermarking, image retrieval/search, editing/transformation (think Photoshop)... and of course video is another can of worms, adding time-series data.
There are strong connections between higher-level image processing and statistical AI and data mining, so you might consider exploring those topics too.