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

In breast cancer research, diverse data types and formats, such as radiological images,
clinical records, histological data, and expression analysis, are employed. Given the intricate …

Learned image compression methods have exhibited superior rate-distortion performance
than classical image compression standards. Most existing learned image compression …

For learned image compression, the autoregressive context model is proved effective in
improving the rate-distortion (RD) performance. Because it helps remove spatial …

Abstract Implicit Neural Representations (INRs) gained attention as a novel and effective
representation for various data types. Recently, prior work applied INRs to image …

Despite considerable progress on end-to-end optimized deep networks for image
compression, video coding remains a challenging task. Recently proposed methods for …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We review a class of methods that can be collected under the name nonlinear transform
coding (NTC), which over the past few years have become competitive with the best linear …

As technology evolves, more components are integrated into printed circuit boards (PCBs)
and the PCB layout increases. Because small defects on signal trace can cause significant …

Traditional video compression approaches build upon the hybrid coding framework with
motion-compensated prediction and residual transform coding. In this paper, we propose the …