  Motivated by recent work on deep neural network (DNN)-based image compression
methods showing potential improvements in image quality, savings in storage,
and bandwidth reduction, we propose to perform image understanding tasks such
as classification and segmentation directly on the compressed representations
produced by these compression methods. Since the encoders and decoders in
DNN-based compression methods are neural networks with feature-maps as internal
representations of the images, we directly integrate these with architectures
for image understanding. This bypasses decoding of the compressed
representation into RGB space and reduces computational cost. Our study shows
that accuracies comparable to networks that operate on compressed RGB images
can be achieved while reducing the computational complexity up to $2\times$.
Furthermore, we show that synergies are obtained by jointly training
compression networks with classification networks on the compressed
representations, improving image quality, classification accuracy, and
segmentation performance. We find that inference from compressed
representations is particularly advantageous compared to inference from
compressed RGB images for aggressive compression rates.
