# Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs

## TL;DR

This paper introduces a hybrid image compression framework combining deep learning and traditional codecs, achieving superior quality over existing methods by effectively encoding images in the RGB444 domain.

## Contribution

It presents a novel layered compression scheme that integrates CNN-based representations with lossless and lossy codecs, improving compression performance.

## Key findings

- Outperforms state-of-the-art deep learning layered coding schemes.
- Achieves higher PSNR and MS-SSIM than traditional codecs like BPG.
- Effective in RGB444 domain across various bit rates.

## Abstract

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.