The emerging conditional coding-based neural video codec (NVC) shows superiority over
commonly-used residual coding-based codec and the latest NVC already claims to …

Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …

Entropy estimation is essential for the performance of learned image compression. It has
been demonstrated that a transformer-based entropy model is of critical importance for …

This paper explores the possibility of extending the capability of pre-trained neural image
compressors (eg adapting to new data or target bitrates) without breaking backward …

End-to-end learned image compression codecs have notably emerged in recent years.
These codecs have demonstrated superiority over conventional methods, showcasing …

Deep learning-based image compression has made great progresses recently. However,
some leading schemes use serial context-adaptive entropy model to improve the rate …

The effective receptive field (ERF) plays an important role in transform coding, which
determines how much redundancy can be removed during transform and how many spatial …

Telemedicine has a critical role in healthcare by supporting the information exchange
between the patients and the physicians as well as between the physicians for consultation …

Modern sensors produce increasingly rich streams of high-resolution data. Due to resource
constraints, machine learning systems discard the vast majority of this information via …

Recent advances in video compression introduce implicit neural representation (INR) based
methods, which effectively capture global dependencies and characteristics of entire video …