Video Quality Metrics

Farias, Márcio Muniz de

doi:10.5772/8038

Cited by 15 publications

(6 citation statements)

References 61 publications

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“…The main goal of each No-Reference approach is to create an estimator based on the proposed features that would predict the MOS of human observes, without using the original image or sequence data. And since the model does not require any comparison of signals, the calculations can be performed in real-time [12]. This feature makes No-Reference models very attractive for measuring user QoE.…”

Section: No-reference Methodsmentioning

confidence: 98%

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A_PSQA: Efficient real-time video streaming QoE tool in a future media internet context

Cherif

Ksentini

Negru

et al. 2011

2011 IEEE International Conference on Multimedia and Expo

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Quality of Experience (QoE) is the key criteria for evaluating the Media Services. Unlike objective Quality of Service (QoS) metrics, QoE is more accurate to reflect the user experience as it considers human visual system and its complex behavior towards distortions in the displayed video sequence. In this paper, we present a new QoE tool solution, named ALICANTE Pseudo Subjective Quality Assessment (A_PSQA). It relies on a No-Reference QoE measuring approach, hybrid between subjective and objective methods fully functional in Future Media Internet context. To validate this approach, we deployed a video streaming platform, and considered different video sequences having different characteristics (low/high motion, quality, etc.). We then compared the results of A_PSQA with two FullReferences methods (SSIM and PSNR) and two NoReferences approaches. Obtained results demonstrate that A_PSQA shows a higher correlation with subjective quality ratings (MOS) than all others methods.

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Section: No-reference Methodsmentioning

confidence: 98%

“…Usual metrics are Peak Signal-toNoise Ratio (PSNR) [2] and Structural SIMilarity (SSIM) Index [2]; -Reduced Reference (RR) metric: distorted and some extracted structural features. Example of features: localized spatial and temporal activity [12]; -No Reference (NR): Only the distorted videos are available [12].…”

Section: Methods For Video Quality Measuringmentioning

confidence: 99%

A_PSQA: Efficient real-time video streaming QoE tool in a future media internet context

Cherif

Ksentini

Negru

et al. 2011

2011 IEEE International Conference on Multimedia and Expo

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“…The performance of FANET networks can be analyzed through computer simulations. In order to correctly evaluate these network types, two scenarios were simulated to simulate different transmission types, and each one used three of the aforementioned routing protocols: AODV, OLSR, and DSDV [28].…”

Section: Performance Evaluationmentioning

confidence: 99%

Wireless Communications Challenges to Flying Ad Hoc Networks (FANET)

Azevedo¹,

Coutinho²,

Toda³

et al. 2020

Mobile Computing

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The increasing demand for Internet access from more and more different devices in recent years has provided a challenge for companies and the academic community to research and develop new solutions that support the increasing flow in the network, applications that require very low latencies and more dynamic and scalable infrastructures, in this context the mobile ad hoc networks (MANETs) emerged as a possible solution and applying this technology in unmanned aerial vehicles (UAVs) was developed the flying ad hoc networks (FANETs) which are wireless networks independent, its main characteristics are to have high mobility, scalability for different applications and scenarios and robustness to deal with possible communication failures. However, they still have several constraints such as limited flight time of UAVs and routing protocols that are capable of supporting network dynamics. To analyze this scenario, two simulations were developed where it was possible to observe the behavior of FANET with different routing protocols both during data transmission and video transmission. The results show that the choice of the best routing protocol must take into account the mobility of the UAVs and the necessary communication priority in the network.

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“…These videos contain errors or variations in coding quality artifacts. Common errors include blocking or blockiness, blurring, color bleeding, DCT basis image effect, staircase effect, ringing, mosquito noise, flickering, packet loss, and jitter [47]. Bitrate and frame rate can be adjusted during the coding process.…”

Section: Fig 2 a Possible Relation Between Qos And Qoe (Based On [1mentioning

confidence: 99%