Residual coding has gained prevalence in lossless compression, where a lossy layer is
initially employed and the reconstruction errors (ie, residues) are then losslessly …

We study the problem of attribute compression for large-scale unstructured 3D point clouds.
Through an in-depth exploration of the relationships between different encoding steps and …

Lossless and near-lossless image compression is of paramount importance to professional
users in many technical fields, such as medicine, remote sensing, precision engineering and …

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

Transformer-based entropy models have gained prominence in recent years due to their
superior ability to capture long-range dependencies in probability distribution estimation …

Lossless image compression is an essential aspect of image processing, particularly in
many fields that require high information fidelity. In recent years, learned lossless image …

Recently, there are significant advancements in learning-based image compression
methods surpassing traditional coding standards. Most of them prioritize achieving the best …

We propose a learned lossless image compression method for high bit-depth medical
imaging (up to 16 bit-depths). Instead of compressing a high bit-depth medical image as a …

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

As a fundamental data format representing spatial information, depth map is widely used in
signal processing and computer vision fields. Massive amount of high precision depth maps …