  We address the challenge of applying existing convolutional neural network
(CNN) architectures to compressed images. Existing CNN architectures represent
images as a matrix of pixel intensities with a specified dimension; this
desired dimension is achieved by downgrading or cropping. Downgrading and
cropping are attractive in that the result is also an image; however, an
algorithm producing an alternative "compressed" representation could yield
better classification performance. This compression algorithm need not be
reversible, but must be compatible with the CNN's operations. This problem is
thus the counterpart of the well-studied problem of applying compressed CNNs to
uncompressed images, which has attracted great interest as CNNs are deployed to
size-, weight-, and power- (SWaP)-limited devices. We introduce Localized
Compression, a generalization of downgrading in which the original image is
divided into blocks and each block is compressed to a smaller size using either
sampling- or random-matrix-based techniques. By aligning the size of the
compressed blocks with the size of the CNN's convolutional region, localized
compression can be made compatible with any CNN architecture. Our experimental
results show that Localized Compression results in classification accuracy
approximately 1-2% higher than is achieved by downgrading to the equivalent
resolution.
