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 …

Image/video coding has been a remarkable research area for both academia and industry
for many years. Testing datasets, especially high-quality image/video datasets are desirable …

The emerging large models have achieved notable progress in the fields of natural
language processing and computer vision. However, large models for neural video coding …

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

Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D
data structures, combined representation learning of 3D point clouds and text remains …

Deep video compression has made remarkable process in recent years, with the majority of
advancements concentrated on P-frame coding. Although efforts to enhance B-frame coding …

In this paper, we present Dense Trajectory Fields (DTF), a novel low-level holistic approach
inspired by optical-flow and trajectory approaches, focusing on both spatial and temporal …

With the high demands for video streaming, recent years have witnessed a growing interest
in utilizing deep learning for video compression. Most existing neural video compression …

Learned video compression methods already outperform VVC in the low-delay (LD) case,
but the random-access (RA) scenario remains challenging. Most works on learned RA video …

In Learned Video Compression (LVC), improving inter prediction, such as enhancing
temporal context mining and mitigating accumulated errors, is crucial for boosting rate …