Implicit neural representations store videos as neural networks and have performed well for
vision tasks such as video compression and denoising. With frame index and/or positional …

The digital age has brought a significant increase in video traffic. This traffic growth, driven
by rapid internet advancements and a surge in multimedia applications, presents both …

Learning-based video compression is currently a popular research topic, offering the
potential to compete with conventional standard video codecs. In this context, Implicit Neural …

InIn HTTP Adaptive Streaming (HAS), each video is divided into smaller segments, and each
segment is encoded at multiple pre-defined bitrates to construct a bitrate ladder. To optimize …

As deep convolutional neural networks (DNNs) are widely used in various fields of computer
vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has …

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

Compression and reconstruction of visual data have been widely studied in the computer
vision community, even before the popularization of deep learning. More recently, some …

Since the future of computing is heterogeneous, scalability is a crucial problem for single
image super-resolution. Recent works try to train one network, which can be deployed on …

Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a
significant challenge in image super-resolution (SR). While recent approaches have …

Video Compression (VC) is a significant aspect of multimedia technology, in which the goal
to minimize the size of video data, while also preserving its perceptual quality, for effective …