Autoencoder-based Image Companding
Alim Wicaksono H.P., Heri Prasetyo, Jing-Ming Guo · 2020
This paper presents a deep learning-based method for effective image companding. The autoencoder inherits the effectiveness of Convolutional Neural Networks (CNN) and residual learning framework to transform High Dynamic Range (HDR) images to Low Dynamic Range (LDR) and its reverse process. Since, the image companding task involves the nondifferentiable operation, thus the encoder and decoder networks are alternately trained using an iterative approach. The experimental result clearly reveals that the proposed method yields a promising result.