Deep learning architectures are now pervasive and filled almost all applications under
image processing, computer vision, and biometrics. The attractive property of feature …

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 …

In the contemporary landscape, where a huge amount of data plays a vital role, the
importance of strong and robust cybersecurity measures has become increasingly …

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 …

The paper proposes a novel frame-wise filtering method based on Convolutional Neural
Networks (CNNs) for enhancing the quality of HEVC decoded videos. A novel deep neural …

Recent learning-based lossless image compression methods encode an image in the unit of
subimages and achieve comparable performances to conventional non-learning algorithms …

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 …

Lifting-based wavelet transform has been extensively used for efficient compression of
various types of visual data. Generally, the performance of such coding schemes strongly …

Visual monitoring supported by the Internet of Things (IoT) increasingly relies on analyzing a
mass of image data with human–machine interactive mechanisms. However, maintaining …