Digital steganography has a long history, starting to be developed in the 90s until now. The
main aspects of early steganography are security, imperceptibility, and payload. Security is …

Learning based video compression attracts increasing attention in the past few years. The
previous hybrid coding approaches rely on pixel space operations to reduce spatial and …

The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering
static objects has inspired numerous attempts on dynamic scenes. Current techniques that …

The previous deep video compression approaches only use the single scale motion
compensation strategy and rarely adopt the mode prediction technique from the traditional …

In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for
both static and dynamic point cloud compression. Taking advantages of both octree based …

Abstract Implicit Neural Representations (INR) or neural fields have emerged as a popular
framework to encode multimedia signals such as images and radiance fields while retaining …

While there are many deep learning based approaches for single image compression, the
field of end-to-end learned video coding has remained much less explored. Therefore, in …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

The remarkable optical properties of metal nanoparticles are governed by the excitation of
localized surface plasmon resonances (LSPRs). The sensitivity of each LSPR mode, whose …

In the learning based video compression approaches, it is an essential issue to compress
pixel-level optical flow maps by develo** new motion vector (MV) encoders. In this work …