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 …

It has long been established that predictive models can be transformed into lossless
compressors and vice versa. Incidentally, in recent years, the machine learning community …

How to predict images is an important issue in the reversible data hiding (RDH) community.
In this letter, we propose a novel CNN-based prediction approach by luminously dividing a …

Abstract Deep Neural Networks (DNN) have emerged in recent year as a best-of-breed
alternative for performing various classification, prediction and identification tasks in images …

There is an increasing number of image data produced in our life nowadays, which creates
a big challenge to store and transmit them. For some fields requiring high fidelity, the …

This paper proposes a novel approach for lossless image compression. The proposed
coding approach employs a deep-learning-based method to compute the prediction for each …

An efficient predictor is essential in reversible data hiding methods. This paper proposes a
video reversible data hiding method, where the correlations between pixels in both spatial …

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural
Networks (CNNs) for lossless video coding. A deep neural network model which follows a …

This paper presents a new lossless image compression method based on the learning of
pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and …

Foundation models have recently been shown to be strong data compressors. However,
when accounting for their excessive parameter count, their compression ratios are actually …