  Lossy image compression has been studied extensively in the context of
typical loss functions such as RMSE, MS-SSIM, etc. However, compression at low
bitrates generally produces unsatisfying results. Furthermore, the availability
of massive public image datasets appears to have hardly been exploited in image
compression. Here, we present a paradigm for eliciting human image
reconstruction in order to perform lossy image compression. In this paradigm,
one human describes images to a second human, whose task is to reconstruct the
target image using publicly available images and text instructions. The
resulting reconstructions are then evaluated by human raters on the Amazon
Mechanical Turk platform and compared to reconstructions obtained using
state-of-the-art compressor WebP. Our results suggest that prioritizing
semantic visual elements may be key to achieving significant improvements in
image compression, and that our paradigm can be used to develop a more
human-centric loss function.
  The images, results and additional data are available at
https://compression.stanford.edu/human-compression
