86 research outputs found
Compression of phase-only holograms with JPEG standard and deep learning
It is a critical issue to reduce the enormous amount of data in the
processing, storage and transmission of a hologram in digital format. In
photograph compression, the JPEG standard is commonly supported by almost every
system and device. It will be favorable if JPEG standard is applicable to
hologram compression, with advantages of universal compatibility. However, the
reconstructed image from a JPEG compressed hologram suffers from severe quality
degradation since some high frequency features in the hologram will be lost
during the compression process. In this work, we employ a deep convolutional
neural network to reduce the artifacts in a JPEG compressed hologram.
Simulation and experimental results reveal that our proposed "JPEG + deep
learning" hologram compression scheme can achieve satisfactory reconstruction
results for a computer-generated phase-only hologram after compression
Augmented Reality Application Supporting On-Site Secondary Building Assets Management
none5sinoneA. Corneli, B. Naticchia, A. Carbonari, F. Bosché, L. PrincipiCorneli, A.; Naticchia, B.; Carbonari, A.; Bosché, F.; Principi, L
Digital watermarking and novel security devices
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Roadmap on 3D integral imaging: Sensing, processing, and display
This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field
MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding
The rapid advancement of artificial intelligence (AI) technology has led to
the prioritization of standardizing the processing, coding, and transmission of
video using neural networks. To address this priority area, the Moving Picture,
Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a
suite of standards called MPAI-EEV for "end-to-end optimized neural video
coding." The aim of this AI-based video standard project is to compress the
number of bits required to represent high-fidelity video data by utilizing
data-trained neural coding technologies. This approach is not constrained by
how data coding has traditionally been applied in the context of a hybrid
framework. This paper presents an overview of recent and ongoing
standardization efforts in this area and highlights the key technologies and
design philosophy of EEV. It also provides a comparison and report on some
primary efforts such as the coding efficiency of the reference model.
Additionally, it discusses emerging activities such as learned
Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under
development, or in the exploration phase. With a focus on UAV video signals,
this paper addresses the current status of these preliminary efforts. It also
indicates development timelines, summarizes the main technical details, and
provides pointers to further points of reference. The exploration experiment
shows that the EEV model performs better than the state-of-the-art video coding
standard H.266/VVC in terms of perceptual evaluation metric
- …