Large Language Models (LLMs), often recognized as AI systems trained on vast amounts of data to efficiently predict the next part of a word, are now being viewed from a different perspective. A recent ...

Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and ...

The CCSDS 122.0-B-1 specification is part of a standards suite developed by the Consultative Committee for Space Data Systems. The CCSDS is formed by the major space agencies of the world for the ...

Image compression has been one of the constantly evolving challenges in computer science. Programers and researchers are always trying to improve current standards or create new ones to get better ...

Given the image on the left, two study participants made the reconstruction on the right. People preferred their reconstruction to the image at the center, a highly compressed version of the original ...

Effective compression is about finding patterns to make data smaller without losing information. When an algorithm or model can accurately guess the next piece of data in a sequence, it shows it’s ...

Optimizing data compression methods has become more critical than ever for cloud storage, data management, and streaming applications. Working with compressed data reduces network bandwidth, data ...

Images transmitted over the world wide web are an excellent example of why data compression is important. Suppose we need to download a digitized color photograph over a computer's 33.6 kbps modem. If ...

In image compression, a large file that could be cumbersome to store or share loses a small amount of visual information. This "lossiness" largely preserves the image while vastly reducing its file ...