  Learned image compression methods generally optimize a rate-distortion loss,
trading off improvements in visual distortion for added bitrate. Increasingly,
however, compressed imagery is used as an input to deep learning networks for
various tasks such as classification, object detection, and superresolution. We
propose a recognition-aware learned compression method, which optimizes a
rate-distortion loss alongside a task-specific loss, jointly learning
compression and recognition networks. We augment a hierarchical
autoencoder-based compression network with an EfficientNet recognition model
and use two hyperparameters to trade off between distortion, bitrate, and
recognition performance. We characterize the classification accuracy of our
proposed method as a function of bitrate and find that for low bitrates our
method achieves as much as 26% higher recognition accuracy at equivalent
bitrates compared to traditional methods such as Better Portable Graphics
(BPG).
