  Most current research in the domain of image compression focuses solely on
achieving state of the art compression ratio, but that is not always usable in
today's workflow due to the constraints on computing resources.
  Constant market requirements for a low-complexity image codec have led to the
recent development and standardization of a lightweight image codec named JPEG
XS.
  In this work we show that JPEG XS compression can be adapted to a specific
given task and content, such as preserving visual quality on desktop content or
maintaining high accuracy in neural network segmentation tasks, by optimizing
its gain and priority parameters using the covariance matrix adaptation
evolution strategy.
