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

Distributed Source Coding Techniques for Lossless Compression of Hyperspectral Images

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems

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

Side Information Generation in Distributed Video Coding

Distributed Video Coding (DVC) coding paradigm is based largely on two theorems of Information Theory and Coding, which are Slepian-wolf theorem and Wyner-Ziv theorem that were introduced in 1973 and 1976 respectively. DVC bypasses the need of performing Motion Compensation (MC) and Motion Estimation (ME) which are largely responsible for the complex encoder in devices. DVC instead relies on exploiting the source statistics, totally/partially, at only the decoder. Wyner-Ziv coding, a particular case of DVC, which is explored in detail in this thesis. In this scenario, two correlated sources are independently encoded, while the encoded streams are decoded jointly at the single decoder exploiting the correlation between them. Although the distributed coding study dates back to 1970’s, but the practical efforts and developments in the field began only last decade. Upcoming applications (like those of video surveillance, mobile camera, wireless sensor networks) can rely on DVC, as they don’t have high computational capabilities and/or high storage capacity. Current coding paradigms, MPEG-x and H.26x standards, predicts the frame by means of Motion Compensation and Motion Estimation which leads to highly complex encoder. Whilst in WZ coding, the correlation between temporally adjacent frames is performed only at the decoder, which results in fairly low complex encoder. The main objective of the current thesis is to investigate for an improved scheme for Side Information (SI) generation in DVC framework. SI frames, available at the decoder are generated through the means of Radial Basis Function Network (RBFN) neural network. Frames are estimated from decoded key frames block-by-block. RBFN network is trained offline using training patterns from different frames collected from standard video sequences