# Deep Residual Autoencoder for quality independent JPEG restoration

## TL;DR

This paper introduces a deep residual autoencoder with Residual-in-Residual Dense Blocks that effectively restores JPEG compressed images regardless of the quality factor, outperforming existing methods across multiple benchmarks.

## Contribution

The proposed model uniquely restores JPEG artifacts independently of the compression quality factor using a two-phase autoencoder approach with shared weights.

## Key findings

- Outperforms state-of-the-art methods on benchmark datasets
- Operates effectively across different JPEG quality factors
- Demonstrates robustness to unseen compression qualities

## Abstract

In this paper we propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) to remove artifacts in JPEG compressed images that is independent from the Quality Factor (QF) used. The proposed approach leverages both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. The proposed model operates in the YCbCr color space and performs JPEG artifact restoration in two phases using two different autoencoders: the first one restores the luma channel exploiting 2D convolutions; the second one, using the restored luma channel as a guide, restores the chroma channels explotining 3D convolutions. Extensive experimental results on three widely used benchmark datasets (i.e. LIVE1, BDS500, and CLASSIC-5) show that our model is able to outperform the state of the art with respect to all the evaluation metrics considered (i.e. PSNR, PSNR-B, and SSIM). This results is remarkable since the approaches in the state of the art use a different set of weights for each compression quality, while the proposed model uses the same weights for all of them, making it applicable to images in the wild where the QF used for compression is unkwnown. Furthermore, the proposed model shows a greater robustness than state-of-the-art methods when applied to compression qualities not seen during training.