  Several deep learned lossy compression techniques have been proposed in the
recent literature. Most of these are optimized by using either MS-SSIM
(multi-scale structural similarity) or MSE (mean squared error) as a loss
function. Unfortunately, neither of these correlate well with human perception
and this is clearly visible from the resulting compressed images. In several
cases, the MS-SSIM for deep learned techniques is higher than say a
conventional, non-deep learned codec such as JPEG-2000 or BPG. However, the
images produced by these deep learned techniques are in many cases clearly
worse to human eyes than those produced by JPEG-2000 or BPG.
  We propose the use of an alternative, deep perceptual metric, which has been
shown to align better with human perceptual similarity. We then propose Deep
Perceptual Compression (DPC) which makes use of an encoder-decoder based image
compression model to jointly optimize on the deep perceptual metric and
MS-SSIM. Via extensive human evaluations, we show that the proposed method
generates visually better results than previous learning based compression
methods and JPEG-2000, and is comparable to BPG. Furthermore, we demonstrate
that for tasks like object-detection, images compressed with DPC give better
accuracy.
