  Over recent years, deep learning-based computer vision systems have been
applied to images at an ever-increasing pace, oftentimes representing the only
type of consumption for those images. Given the dramatic explosion in the
number of images generated per day, a question arises: how much better would an
image codec targeting machine-consumption perform against state-of-the-art
codecs targeting human-consumption? In this paper, we propose an image codec
for machines which is neural network (NN) based and end-to-end learned. In
particular, we propose a set of training strategies that address the delicate
problem of balancing competing loss functions, such as computer vision task
losses, image distortion losses, and rate loss. Our experimental results show
that our NN-based codec outperforms the state-of-the-art Versa-tile Video
Coding (VVC) standard on the object detection and instance segmentation tasks,
achieving -37.87% and -32.90% of BD-rate gain, respectively, while being fast
thanks to its compact size. To the best of our knowledge, this is the first
end-to-end learned machine-targeted image codec.
