Neural image compression leverages deep neural networks to outperform traditional image
codecs in rate-distortion performance. However, the resulting models are also heavy …

We propose the deep progressive image compression using trit-planes (DPICT) algorithm,
which is the first learning-based codec supporting fine granular scalability (FGS). First, we …

Trit-plane coding enables deep progressive image compression, but it cannot use
autoregressive context models. In this paper, we propose the context-based trit-plane coding …

Progressive deep image compression (DIC) with hybrid contexts is an under-investigated
problem that aims to jointly maximize the utility of a compressed image for multiple contexts …

This article proposes the scalable cross-modality compression (SCMC) paradigm, in which
the image compression problem is further cast into a representation task by hierarchically …

Progressive compression allows images to start loading as low-resolution versions,
becoming clearer as more data is received. This increases user experience when, for …

Since massive numbers of images are now being communicated from, and stored in
different cloud systems, faster retrieval has become extremely important. This is more …

Progressive coding is essential to the practical deployment of learned image compression
over heterogeneous networks and clients. Existing methods for learned progressive image …

Explainable neural models have gained a lot of attention in recent years. However,
conventional encoder–decoder models do not capture information regarding the importance …

When training end-to-end learned models for lossy compression, one has to balance the
rate and distortion losses. This is typically done by manually setting a tradeoff parameter b …