  Today, many image coding scenarios do not have a human as final intended
user, but rather a machine fulfilling computer vision tasks on the decoded
image. Thereby, the primary goal is not to keep visual quality but maintain the
task accuracy of the machine for a given bitrate. Due to the tremendous
progress of deep neural networks setting benchmarking results, mostly neural
networks are employed to solve the analysis tasks at the decoder side.
Moreover, neural networks have also found their way into the field of image
compression recently. These two developments allow for an end-to-end training
of the neural compression network for an analysis network as information sink.
Therefore, we first roll out such a training with a task-specific loss to
enhance the coding performance of neural compression networks. Compared to the
standard VVC, 41.4% of bitrate are saved by this method for Mask R-CNN as
analysis network on the uncompressed Cityscapes dataset. As a main
contribution, we propose LSMnet, a network that runs in parallel to the encoder
network and masks out elements of the latent space that are presumably not
required for the analysis network. By this approach, additional 27.3% of
bitrate are saved compared to the basic neural compression network optimized
with the task loss. In addition, we are the first to utilize a feature-based
distortion in the training loss within the context of machine-to-machine
communication, which allows for a training without annotated data. We provide
extensive analyses on the Cityscapes dataset including cross-evaluation with
different analysis networks and present exemplary visual results. Inference
code and pre-trained models are published at
https://github.com/FAU-LMS/NCN_for_M2M.
