  Video and image coding for machines (VCM) is an emerging field that aims to
develop compression methods resulting in optimal bitstreams when the decoded
frames are analyzed by a neural network. Several approaches already exist
improving classic hybrid codecs for this task. However, neural compression
networks (NCNs) have made an enormous progress in coding images over the last
years. Thus, it is reasonable to consider such NCNs, when the information sink
at the decoder side is a neural network as well. Therefore, we build-up an
evaluation framework analyzing the performance of four state-of-the-art NCNs,
when a Mask R-CNN is segmenting objects from the decoded image. The compression
performance is measured by the weighted average precision for the Cityscapes
dataset. Based on that analysis, we find that networks with leaky ReLU as
non-linearity and training with SSIM as distortion criteria results in the
highest coding gains for the VCM task. Furthermore, it is shown that the
GAN-based NCN architecture achieves the best coding performance and even
out-performs the recently standardized Versatile Video Coding (VVC) for the
given scenario.
