The usage of media such as images and videos has been extensively increased in recent
years. It has become impractical to store images and videos acquired by camera sensors in …

The autoregressive model has been widely used in learning-based image compression due
to its superior context modeling capability. However, its sequential processing nature also …

The compression of images for efficient storage and transmission is crucial in handling large
data volumes. Lossy image compression reduces storage needs but introduces perceptible …

The quality of the Magnetic Resonance Imaging (MRI) image influences the disease
diagnosis and consequent treatment. However, noise distortion severely impacts these …

This survey articles focuses on emerging connections between the fields of machine
learning and data compression. While fundamental limits of classical (lossy) data …

Point clouds are effective data structures for the representation of three-dimensional media
and hence adopted in a wide range of practical applications. In many cases, the portrayed …

Lossy image compression algorithms are usually employed to reduce the storage space
required by the large number of digital pictures that are acquired and stored daily on digital …

We describe a search-free resizing framework that can further improve the rate-distortion
tradeoff of recent learned image compression models. Our approach is simple: compose a …

Learning-based image codecs produce different compression artifacts, when compared to
the blocking and blurring degradation introduced by conventional image codecs, such as …

Subjective image quality assessment studies are used in many scenarios, such as the
evaluation of compression, super-resolution, and denoising solutions. Among the available …