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 …

Recent advances in learning-based methods have markedly enhanced the capabilities of
image compression. However, these methods struggle with high bit-depth volumetric …

Abstract Autoregressive Initial Bits (ArIB) a framework that combines subimage
autoregression and latent variable models has shown its advantages in lossless image …

This paper presents a method for lossless compression of images with fast decoding time
and the option to select encoder parameters for individual image characteristics to increase …

Modern data compression methods are slowly reaching their limits after 80 years of
research, millions of papers, and wide range of applications. Yet, the extravagant 6G …

Lossless image compression is an important research field in image compression. Recently,
learning-based lossless image compression methods achieved impressive performance …

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

Convolutional neural networks have demonstrated impressive results in many computer
vision tasks. However, the increasing size of these networks raises concerns about the …

Recently, DNN models for lossless image coding have surpassed their traditional
counterparts in compression performance, reducing the bit rate by about ten percent for …

In this work, we propose a novel principal component approximation network (PCANet) for
image compression. The proposed network is based on the assumption that a set of images …