  This paper reviews the first challenge on efficient perceptual image
enhancement with the focus on deploying deep learning models on smartphones.
The challenge consisted of two tracks. In the first one, participants were
solving the classical image super-resolution problem with a bicubic downscaling
factor of 4. The second track was aimed at real-world photo enhancement, and
the goal was to map low-quality photos from the iPhone 3GS device to the same
photos captured with a DSLR camera. The target metric used in this challenge
combined the runtime, PSNR scores and solutions' perceptual results measured in
the user study. To ensure the efficiency of the submitted models, we
additionally measured their runtime and memory requirements on Android
smartphones. The proposed solutions significantly improved baseline results
defining the state-of-the-art for image enhancement on smartphones.
