Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Demo : distributed video coding applications in wireless multimedia sensor networks

Novel distributed video coding (DVC) architectures developed by the IBBT DVC group realize state-of-the-art video coding efficiency under stringent energy restrictions, while supporting error-resilience and scalability. Therefore, these architectures are particularly attractive for application scenarios involving low-complexity energy-constrained wireless visual sensors. This demo presents the scenarios, which are considered to be the most promising areas of integration for IBBT's DVC systems, considering feasibility and commercial applicability

DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression

We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources. Its compression capability is much better than the method of training codecs separately. Meanwhile, the performance of our distributed system with 10 distributed sources is only within 2 dB peak signal-to-noise ratio (PSNR) of the performance of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our data-driven methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning

Video Compression for Camera Networks: A Distributed Approach

The problem of finding efficient communications techniques to distribute multi-view video content across different devices and users in a network is receiving a great attention in the last years. Much interest in particular has been devoted recently to the so called field of Distributed Video Coding (DVC). After briefly reporting traditional approaches to multiview coding, this chapter will introduce the field of DVC for multi-camera systems. The theoretical background of Distributed Source Coding (DSC) is first concisely presented and the problem of the application of DSC principles to the case of video sources is then analyzed. The topic is presented discussing approaches to the problem of DVC in both single-view and in multi-view applications

On the Design of Perceptual MPEG-Video Encryption Algorithms

In this paper, some existing perceptual encryption algorithms of MPEG videos are reviewed and some problems, especially security defects of two recently proposed MPEG-video perceptual encryption schemes, are pointed out. Then, a simpler and more effective design is suggested, which selectively encrypts fixed-length codewords (FLC) in MPEG-video bitstreams under the control of three perceptibility factors. The proposed design is actually an encryption configuration that can work with any stream cipher or block cipher. Compared with the previously-proposed schemes, the new design provides more useful features, such as strict size-preservation, on-the-fly encryption and multiple perceptibility, which make it possible to support more applications with different requirements. In addition, four different measures are suggested to provide better security against known/chosen-plaintext attacks.Comment: 10 pages, 5 figures, IEEEtran.cl

Livrable D3.4 of the PERSEE project : 2D coding tools final report

Livrable D3.4 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.4 du projet. Son titre : 2D coding tools final repor