The past decade has witnessed the great success of deep learning in many disciplines,
especially in computer vision and image processing. However, deep learning-based video …

In the field of image processing and computer vision (CV), machine learning (ML)
architectures are widely used. Image compression problems can be solved using …

Image compression is one of the most fundamental techniques and commonly used
applications in the image and video processing field. Earlier methods built a well-designed …

Image compression is a method to remove spatial redundancy between adjacent pixels and
reconstruct a high-quality image. In the past few years, deep learning has gained huge …

Deep neural networks represent a powerful class of function approximators that can learn to
compress and reconstruct images. Existing image compression algorithms based on neural …

Nowadays, image and video are the data types that consume most of the resources of
modern communication channels, both in fixed and wireless networks. Thus, it is vital to …

Traditional image and video compression methods are designed to maintain the quality of
human visual perception, which makes it necessary to reconstruct the image or video before …

In this paper, we propose to use deep neural networks for image compression in the wavelet
transform domain. When the input image is transformed from the spatial pixel domain to the …

We propose a method for learned compression artifact removal by post-processing of BPG
compressed images. We trained three networks of different sizes. We encoded input images …

This paper presents an image coding method using deep convolutional neural networks and
the fractional wavelet transform (FrWT) algorithm. FrWT requires less memory than …