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

Resource Allocation and Performance Analysis of Wireless Video Sensors

Digital Object Identifier 10.1109/TCSVT.2006.873154Wireless video sensor networks (WVSNs) have been envisioned for a wide range of important applications, including battlefield intelligence, security monitoring, emergency response, and environmental tracking. Compared to traditional communication system, the WVSN operates under a set of unique resource constraints, including limitations with respect to energy supply,on-board computational capability, and transmission bandwidth. The objective of this paper is to study the resource utilization behavior of a wireless video sensor and analyze its performance under the resource constraints. More specifically, we develop an analytic power-rate-distortion (P-R-D) model to characterize the inherent relationship between the power consumption of a video encoder and its rate-distortion performance. Based on the P-R-D analysis and a simplified model for wireless transmission power,we study the optimum power allocation between video encoding and wireless transmission and introduce a measure called achievable minimum distortion to quantify the distortion under a total power constraint. We consider two scenarios in wireless video sensing, small-delay wireless video monitoring and large-delay wireless video surveillance, and analyze the performance limit of the wireless video sensor in each scenario. The analysis and results obtained in this paper provide an important guideline for practical wireless video sensor design.This work was supported in part by the National Science Foundation under Grant DBI-0529082 and Grant DBI-0529012

Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

On the Effectiveness of Video Recolouring as an Uplink-model Video Coding Technique

For decades, conventional video compression formats have advanced via incremental improvements with each subsequent standard achieving better rate-distortion (RD) efficiency at the cost of increased encoder complexity compared to its predecessors. Design efforts have been driven by common multi-media use cases such as video-on-demand, teleconferencing, and video streaming, where the most important requirements are low bandwidth and low video playback latency. Meeting these requirements involves the use of computa- tionally expensive block-matching algorithms which produce excellent compression rates and quick decoding times. However, emerging use cases such as Wireless Video Sensor Networks, remote surveillance, and mobile video present new technical challenges in video compression. In these scenarios, the video capture and encoding devices are often power-constrained and have limited computational resources available, while the decoder devices have abundant resources and access to a dedicated power source. To address these use cases, codecs must be power-aware and offer a reasonable trade-off between video quality, bitrate, and encoder complexity. Balancing these constraints requires a complete rethinking of video compression technology. The uplink video-coding model represents a new paradigm to address these low-power use cases, providing the ability to redistribute computational complexity by offloading the motion estimation and compensation steps from encoder to decoder. Distributed Video Coding (DVC) follows this uplink model of video codec design, and maintains high quality video reconstruction through innovative channel coding techniques. The field of DVC is still early in its development, with many open problems waiting to be solved, and no defined video compression or distribution standards. Due to the experimental nature of the field, most DVC codec to date have focused on encoding and decoding the Luma plane only, which produce grayscale reconstructed videos. In this thesis, a technique called “video recolouring” is examined as an alternative to DVC. Video recolour- ing exploits the temporal redundancies between colour planes, reducing video bitrate by removing Chroma information from specific frames and then recolouring them at the decoder. A novel video recolouring algorithm called Motion-Compensated Recolouring (MCR) is proposed, which uses block motion estimation and bi-directional weighted motion-compensation to reconstruct Chroma planes at the decoder. MCR is used to enhance a conventional base-layer codec, and shown to reduce bitrate by up to 16% with only a slight decrease in objective quality. MCR also outperforms other video recolouring algorithms in terms of objective video quality, demonstrating up to 2 dB PSNR improvement in some cases

A Realistic Coverage Model with Backup Set Computation for Wireless Video Sensor Network

Wireless Video Sensor Network (WVSN) are gaining increasing popularity due availability of low cost CMOS camera and miniaturization of hardware. For many applications it is difficult to have pre-engineered deployment of video camera sensors which leads to redundancy. Due to sectored coverage and random deployment, it becomes challenging to model video sensor coverage to identify redundancy and suppress redundant video transmission. Several efforts have been made to model coverage redundancy considering 2-dimensional coverage. Field of View (FoV) of the camera sensor is in 3-dimensions, thus it is very difficult to model the coverage and identify overlap area for realistic camera. 3-dimensional coverage is largely an unexplored problem. In this paper, a realistic 3-dimensional pyramid camera coverage is assumed and backup set of nodes are computed. Backup set of a node is a set of video sensor nodes which collectively covers coverage area of the node under consideration. The approach presented in the paper identifies minimal sized set of backup nodes which can be used to adaptively duty cycle the video capture and transmission. The result shows that number of nodes required to remain active to cover the sensor field is reduced and in turn average energy consumption of the network also reduces

Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim of this research is to investigate the use of adaptive Compressive Sensing (CS) for efficient target detection and tracking in WVSN-based surveillance applications. CS is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In addition, adaptive CS dynamically chooses variable compression rates according to different data sets to represent captured images in an efficient way hence saving energy and memory space. In this work, a literature review on compressive sensing, target detection and tracking for WVSN is carried out to investigate existing techniques. Only single view target tracking is considered to keep minimum number of visual sensor nodes in a wake-up state to optimize the use of nodes and save battery life which is limited in WVSNs. To reduce the size of captured images an adaptive block CS technique is proposed and implemented to compress the high volume data images before being transmitted through the wireless channel. The proposed technique divides the image to blocks and adaptively chooses the compression rate for relative blocks containing the target according to the sparsity nature of images. At the receiver side, the compressed image is then reconstructed and target detection and tracking are performed to investigate the effect of CS on the tracking performance. Least mean square adaptive filter is used to predicts target’s next location, an iterative quantized clipped LMS technique is proposed and compared with other variants of LMS and results have shown that it achieved lower error rates than other variants of lMS. The tracking is performed in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block compressive sensing (CS) up to 31% measurements of data are required to be transmitted for less sparse images and 15% for more sparse, while preserving the 33dB image quality and the required detection and tracking performance. Adaptive CS resulted in 82% energy saving as compared to transmitting the required image with no C

Hardware realization of discrete wavelet transform cauchy Reed Solomon minimal instruction set computer architecture for wireless visual sensor networks

Large amount of image data transmitting across the Wireless Visual Sensor Networks (WVSNs) increases the data transmission rate thus increases the power transmission. This would inevitably decreases the operating lifespan of the sensor nodes and affecting the overall operation of WVSNs. Limiting power consumption to prolong battery lifespan is one of the most important goals in WVSNs. To achieve this goal, this thesis presents a novel low complexity Discrete Wavelet Transform (DWT) Cauchy Reed Solomon (CRS) Minimal Instruction Set Computer (MISC) architecture that performs data compression and data encoding (encryption) in a single architecture. There are four different programme instructions were developed to programme the MISC processor, which are Subtract and Branch if Negative (SBN), Galois Field Multiplier (GF MULT), XOR and 11TO8 instructions. With the use of these programme instructions, the developed DWT CRS MISC were programmed to perform DWT image compression to reduce the image size and then encode the DWT coefficients with CRS code to ensure data security and reliability. Both compression and CRS encoding were performed by a single architecture rather than in two separate modules which require a lot of hardware resources (logic slices). By reducing the number of logic slices, the power consumption can be subsequently reduced. Results show that the proposed new DWT CRS MISC architecture implementation requires 142 Slices (Xilinx Virtex-II), 129 slices (Xilinx Spartan-3E), 144 Slices (Xilinx Spartan-3L) and 66 Slices (Xilinx Spartan-6). The developed DWT CRS MISC architecture has lower hardware complexity as compared to other existing systems, such as Crypto-Processor in Xilinx Spartan-6 (4828 Slices), Low-Density Parity-Check in Xilinx Virtex-II (870 slices) and ECBC in Xilinx Spartan-3E (1691 Slices). With the use of RC10 development board, the developed DWT CRS MISC architecture can be implemented onto the Xilinx Spartan-3L FPGA to simulate an actual visual sensor node. This is to verify the feasibility of developing a joint compression, encryption and error correction processing framework in WVSNs

Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks

This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks. Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor collaboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. Besides, the energy resource on the WVSNs is also very tight, with wireless communication and complicated computation consuming most energy in WVSNs. Therefore the feature-based image comparison method has been proposed, which directly compares the captured image from each visual sensor in an economical way in terms of both the computational cost and the transmission overhead. The feature-based image comparison method compares different images and aims to find similar image pairs using a set of local features from each image. The image feature is a numerical representation of the raw image and can be more compact in terms of the data volume than the raw image. The feature-based image comparison contains three steps: feature detection, descriptor calculation and feature comparison. For the step of feature detection, the dissertation proposes two computationally efficient corner detectors. The first detector is based on the Discrete Wavelet Transform that provides multi-scale corner point detection and the scale selection is achieved efficiently through a Gaussian convolution approach. The second detector is based on a linear unmixing model, which treats a corner point as the intersection of two or three “line” bases in a 3 by 3 region. The line bases are extracted through a constrained Nonnegative Matrix Factorization (NMF) approach and the corner detection is accomplished through counting the number of contributing bases in the linear mixture. For the step of descriptor calculation, the dissertation proposes an effective dimensionality reduction algorithm for the high dimensional Scale Invariant Feature Transform (SIFT) descriptors. A set of 40 SIFT descriptor bases are extracted through constrained NMF from a large training set and all SIFT descriptors are then projected onto the space spanned by these bases, achieving dimensionality reduction. The efficiency of the proposed corner detectors have been proven through theoretical analysis. In addition, the effectiveness of the proposed corner detectors and the dimensionality reduction approach has been validated through extensive comparison with several state-of-the-art feature detector/descriptor combinations

Defenses against Covert-Communications in Multimedia and Sensor Networks

Steganography and covert-communications represent a great and real threat today more than ever due to the evolution of modern communications. This doctoral work proposes defenses against such covert-communication techniques in two threatening but underdeveloped domains. Indeed, this work focuses on the novel problem of visual sensor network steganalysis but also proposes one of the first solutions against video steganography. The first part of the dissertation looks at covert-communications in videos. The contribution of this study resides in the combination of image processing using motion vector interpolation and non-traditional detection theory to obtain better results in identifying the presence of embedded messages in videos compared to what existing still-image steganalytic solutions would offer. The proposed algorithm called MoViSteg utilizes the specifics of video, as a whole and not as a series of images, to decide on the occurrence of steganography. Contrary to other solutions, MoViSteg is a video-specific algorithm, and not a repetitive still-image steganalysis, and allows for detection of embedding in partially corrupted sequences. This dissertation also lays the foundation for the novel study of visual sensor network steganalysis. We develop three different steganalytic solutions to the problem of covert-communications in visual sensor networks. Because of the inadequacy of the existing steganalytic solutions present in the current research literature, we introduce the novel concept of preventative steganalysis, which aims at discouraging potential steganographic attacks. We propose a set of solutions with active and passive warden scenarii using the material made available by the network. To quantify the efficiency of the preventative steganalysis, a new measure for evaluating the risk of steganography is proposed: the embedding potential which relies on the uncertainty of the image’s pixel values prone to corruption