We introduce new transforms for efficient compression of image blocks with directional preferences. Each transform, which is an orthogonal basis for a specific direction, is constructed from an eigen-decomposition of a discrete directional Laplacian system matrix.
Articles
Related Articles
February 8, 2021
A Study on MIMO Channel Estimation by 2D and 3D Convolutional Neural Networks
In this paper we study the usage of Convolutional Neural Network (CNN) estimators for the task...
Read More >
1 MIN READING
February 8, 2021
Low PAPR Waveform Design for OFDM Systems Based on Convolutional Autoencoder
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power...
Read More >
1 MIN READING
February 26, 2024
LINEAR LOG-NORMAL ATTENTION WITH UNBIASED CONCENTRATION
Transformer models have achieved remarkable results in a wide range of applications. However, their scalability is...
Read More >
1 MIN READING