  Most image data available are often stored in a compressed format, from which
JPEG is the most widespread. To feed this data on a convolutional neural
network (CNN), a preliminary decoding process is required to obtain RGB pixels,
demanding a high computational load and memory usage. For this reason, the
design of CNNs for processing JPEG compressed data has gained attention in
recent years. In most existing works, typical CNN architectures are adapted to
facilitate the learning with the DCT coefficients rather than RGB pixels.
Although they are effective, their architectural changes either raise the
computational costs or neglect relevant information from DCT inputs. In this
paper, we examine different ways of speeding up CNNs designed for DCT inputs,
exploiting learning strategies to reduce the computational complexity by taking
full advantage of DCT inputs. Our experiments were conducted on the ImageNet
dataset. Results show that learning how to combine all DCT inputs in a
data-driven fashion is better than discarding them by hand, and its combination
with a reduction of layers has proven to be effective for reducing the
computational costs while retaining accuracy.
