Raw Instinct: Trust Your Classifiers and Skip the Conversion
Christos Kantas Bjørk Antoniussen Mathias V. Andersen Rasmus Munksø Shobhit Kotnala Simon B. Jensen Andreas Møgelmose Lau Nørgaard Thomas B. Moeslund
Using RAW-images in computer vision problems is surprisingly underexplored
considering that converting from RAW to RGB does not introduce any new capture
information. In this paper, we show that a sufficiently advanced classifier can
yield equivalent results on RAW input compared to RGB and present a new public
dataset consisting of RAW images and the corresponding converted RGB images.
Classifying images directly from RAW is attractive, as it allows for skipping
the conversion to RGB, lowering computation time significantly. Two CNN
classifiers are used to classify the images in both formats, confirming that
classification performance can indeed be preserved. We furthermore show that
the total computation time from RAW image data to classification results for
RAW images can be up to 8.46 times faster than RGB. These results contribute to
the evidence found in related works, that using RAW images as direct input to
computer vision algorithms looks very promising.