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 …

Image compression for machine and human vision (ICMH) has gained increasing attention
in recent years. Existing ICMH methods are limited by high training and storage overheads …

Recent advances in implicit neural representation (INR)-based video coding have
demonstrated its potential to compete with both conventional and other learning-based …

Content-adaptive compression is crucial for enhancing the adaptability of the pre-trained
neural codec for various contents. Though, its application in neural video compression …

Abstract Learning-based Neural Video Codecs (NVCs) have emerged as a compelling
alternative to standard video codecs, demonstrating promising performance, and simple and …

Parameter-Efficient-Tuning (PET) for pre-trained deep models (eg, transformer) hold
significant potential for domain increment learning (DIL). Recent prevailing approaches …

Learning-based Neural Video Codecs (NVCs) have emerged as a compelling alternative to
the standard video codecs, demonstrating promising performance, and simple and easily …

Lossy image compression networks aim to minimize the latent entropy of images while
adhering to specific distortion constraints. However, optimizing the neural network can be …

Learned image compression (LIC) has achieved state-of-the-art rate-distortion performance,
deemed promising for next-generation image compression techniques. However, pre …

Current test-or compression-time adaptation image compression (TTA-IC) approaches,
which leverage both latent and decoder refinements as a two-step adaptation scheme, have …