# A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression

## TL;DR

This paper introduces a novel deep learning workflow combining GANs and Actor-Critic reinforcement learning to enhance low-rate high-resolution image compression, aiming to maximize PSNR without relying solely on differentiable metrics.

## Contribution

It presents a hybrid deep learning approach that integrates adversarial networks with reinforcement learning to improve image compression quality.

## Key findings

- Effective enhancement of high-resolution images from low-resolution JPEGs.
- Demonstrated potential for maximizing non-differentiable metrics like PSNR.
- Opened new avenues for end-to-end optimization in image compression.

## Abstract

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.