  In recent years, there has been a sharp increase in transmission of images to
remote servers specifically for the purpose of computer vision. In many
applications, such as surveillance, images are mostly transmitted for automated
analysis, and rarely seen by humans. Using traditional compression for this
scenario has been shown to be inefficient in terms of bit-rate, likely due to
the focus on human based distortion metrics. Thus, it is important to create
specific image coding methods for joint use by humans and machines. One way to
create the machine side of such a codec is to perform feature matching of some
intermediate layer in a Deep Neural Network performing the machine task. In
this work, we explore the effects of the layer choice used in training a
learnable codec for humans and machines. We prove, using the data processing
inequality, that matching features from deeper layers is preferable in the
sense of rate-distortion. Next, we confirm our findings empirically by
re-training an existing model for scalable human-machine coding. In our
experiments we show the trade-off between the human and machine sides of such a
scalable model, and discuss the benefit of using deeper layers for training in
that regard.
