  We introduce a general method of performing Residual Network inference and
learning in the JPEG transform domain that allows the network to consume
compressed images as input. Our formulation leverages the linearity of the JPEG
transform to redefine convolution and batch normalization with a tune-able
numerical approximation for ReLu. The result is mathematically equivalent to
the spatial domain network up to the ReLu approximation accuracy. A formulation
for image classification and a model conversion algorithm for spatial domain
networks are given as examples of the method. We show that the sparsity of the
JPEG format allows for faster processing of images with little to no penalty in
the network accuracy.
