SenseVid : A traffic trace based tool for QoE Video transmission assessment dedicated to Wireless Video Sensor Networks

Maimour, Moufida

doi:10.1016/j.simpat.2018.06.006

Cited by 14 publications

(18 citation statements)

References 37 publications

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“…In order to suite the constrained nature of processing resources of low-power motes, we made use of a low complexity image compression provided by SenseVid [8], a video transmission and evaluation tool that considers WVSN specific characteristics. First, each captured image is decomposed into blocks of 8 × 8.…”

Section: Low Complexity Compression and Qoe Metricsmentioning

confidence: 99%

QoE-based Performance Evaluation of Video Transmission using RPL in the IoMT

Kettouche

Maimour

Derdouri

2019

2019 7th Mediterranean Congress of Telecommunications (CMT)

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Internet of Things systems can not successfully realise the notion of ubiquitous connectivity of everything if they are not capable to truly include multimedia things. In this paper, we investigate the feasibility of video streaming using RPL (Routing Protocol for Low Power and Lossy Networks) by focusing on the impact of radio duty cycling on the quality of received video and the energy footprint of the network. To do so, we adopt a low complexity compression technique and use Cooja simulator to carry out our experiments. We use quality of service (QoS) as well as quality of experience (QoE) metrics to evaluate the quality of the received video. We mainly show that RPL along with ContikiMAC, the Contiki default radio duty cycling do not allow to handle real time video transmission in the context of constrained networks. However low rates up to 35 frames per minute are still possible with an acceptable quality.

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Section: Low Complexity Compression and Qoe Metricsmentioning

confidence: 99%

QoE-based Performance Evaluation of Video Transmission using RPL in the IoMT

Kettouche

Maimour

Derdouri

2019

2019 7th Mediterranean Congress of Telecommunications (CMT)

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“…This work aims to investigate the development of moving object detection over WVSN. The Discrete Cosine Transform (DCT) [14] is a frequently utilised image compression technique over WVSN [15,16]. The DCT algorithm converts signals from the spatial domain to a frequency domain representation.…”

Section: Introductionmentioning

confidence: 99%

“…• A new compression-based background subtraction called Spectral Dual Mode Background Subtraction (SDMBS) uses Partial Discrete Cosine Transform (PDCT) [15] (for dimensionality reduction) and Dual mode SGM [17] (for accuracy) to model the background and distinguish the foreground from the background.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

AbdelTawab¹,

Abdelhalim²,

Habib³

2022

IJACSA

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Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

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“…QoE measures were peak signal-to-noise (PSNR), structural similarity index measurement (SSIM) and image difference (ID). PSNR in dB is determined by [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…where m and n represent the image dimensions and i, j represent a pixel's location on the image. The structural similarity index (SSIM) for measuring image quality for image windows (x and y) of the same dimension from an image is determined by [11,13]:…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

Salama¹,

Saatchi²

2019

Technologies

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Video transmission over wireless computer networks is increasingly popular as new applications emerge and wireless networks become more widespread and reliable. An ability to quantify the quality of a video transmitted using a wireless computer network is important for determining network performance and its improvement. The process requires analysing the images making up the video from the point of view of noise and associated distortion as well as traffic parameters represented by packet delay, jitter and loss. In this study a modular fuzzy logic based system was developed to quantify the quality of video transmission over a wireless computer network. Peak signal to noise ratio, structural similarity index and image difference were used to represent the user's quality of experience (QoE) while packet delay, jitter and percentage packet loss ratio were used to represent traffic related quality of service (QoS). An overall measure of the video quality was obtained by combining QoE and QoS values. Systematic sampling was used to reduce the number of images processed and a novel scheme was devised whereby the images were partitioned to more sensitively localize distortions. To further validate the developed system, a subjective test involving 25 participants graded the quality of the received video. The image partitioning significantly improved the video quality evaluation. The subjective test results correlated with the developed fuzzy logic approach. The video quality assessment developed in this study was compared against a method that uses spatial efficient entropic differencing and consistent results were observed. The study indicated that the developed fuzzy logic approaches could accurately determine the quality of a wirelessly transmitted video.

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SenseVid : A traffic trace based tool for QoE Video transmission assessment dedicated to Wireless Video Sensor Networks

Cited by 14 publications

References 37 publications

QoE-based Performance Evaluation of Video Transmission using RPL in the IoMT

QoE-based Performance Evaluation of Video Transmission using RPL in the IoMT

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Evaluation of Wirelessly Transmitted Video Quality Using a Modular Fuzzy Logic System

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