Image compression is an essential approach for decreasing the size in bytes
of the image without deteriorating the quality of it. Typically, classic
algorithms are used but recently deep-learning has been successfully applied.
In this work, is presented a deep super-resolution work-flow for image
compression that maps low-resolution JPEG image to the high-resolution. The
pipeline consists of two components: first, an encoder-decoder neural network
learns how to transform the downsampling JPEG images to high resolution.
Second, a combination between Generative Adversarial Networks (GANs) and
reinforcement learning Actor-Critic (A3C) loss pushes the encoder-decoder to
indirectly maximize High Peak Signal-to-Noise Ratio (PSNR). Although PSNR is a
fully differentiable metric, this work opens the doors to new solutions for
maximizing non-differential metrics through an end-to-end approach between
encoder-decoder networks and reinforcement learning policy gradient methods.