  We propose a versatile deep image compression network based on Spatial
Feature Transform (SFT arXiv:1804.02815), which takes a source image and a
corresponding quality map as inputs and produce a compressed image with
variable rates. Our model covers a wide range of compression rates using a
single model, which is controlled by arbitrary pixel-wise quality maps. In
addition, the proposed framework allows us to perform task-aware image
compressions for various tasks, e.g., classification, by efficiently estimating
optimized quality maps specific to target tasks for our encoding network. This
is even possible with a pretrained network without learning separate models for
individual tasks. Our algorithm achieves outstanding rate-distortion trade-off
compared to the approaches based on multiple models that are optimized
separately for several different target rates. At the same level of
compression, the proposed approach successfully improves performance on image
classification and text region quality preservation via task-aware quality map
estimation without additional model training. The code is available at the
project website: https://github.com/micmic123/QmapCompression
