Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

Valderrama, Diego José Luis Botía; Rúa, Natalia Eugenia Gómez

doi:10.1155/2016/1730814

Cited by 6 publications

(7 citation statements)

References 33 publications

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“…The ratio of data distribution, number of layers, number of neurons resulting from training, and simulation tests show the results of the final programmed algorithm and the results obtained by looking for the proper functions and selecting the correct data layout. The number of repeats chosen was also based on the results of the authors [ 38 ].…”

Section: The Classifier Based On An Artificial Intelligencementioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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Video quality evaluation needs a combined approach that includes subjective and objective metrics, testing, and monitoring of the network. This paper deals with the novel approach of mapping quality of service (QoS) to quality of experience (QoE) using QoE metrics to determine user satisfaction limits, and applying QoS tools to provide the minimum QoE expected by users. Our aim was to connect objective estimations of video quality with the subjective estimations. A comprehensive tool for the estimation of the subjective evaluation is proposed. This new idea is based on the evaluation and marking of video sequences using the sentinel flag derived from spatial information (SI) and temporal information (TI) in individual video frames. The authors of this paper created a video database for quality evaluation, and derived SI and TI from each video sequence for classifying the scenes. Video scenes from the database were evaluated by objective and subjective assessment. Based on the results, a new model for prediction of subjective quality is defined and presented in this paper. This quality is predicted using an artificial neural network based on the objective evaluation and the type of video sequences defined by qualitative parameters such as resolution, compression standard, and bitstream. Furthermore, the authors created an optimum mapping function to define the threshold for the variable bitrate setting based on the flag in the video, determining the type of scene in the proposed model. This function allows one to allocate a bitrate dynamically for a particular segment of the scene and maintains the desired quality. Our proposed model can help video service providers with the increasing the comfort of the end users. The variable bitstream ensures consistent video quality and customer satisfaction, while network resources are used effectively. The proposed model can also predict the appropriate bitrate based on the required quality of video sequences, defined using either objective or subjective assessment.

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Section: The Classifier Based On An Artificial Intelligencementioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

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“…Intrusive prediction models require access to the source, for example, peak signal to noise ratio (PSNR), structural similarity index (SSIM) whereas non-intrusive models do not and, hence, are more appropriate for real-time applications. From the literature, there are several other artificial intelligent techniques (machine learning-based) used to measure video quality, such as random neural networks, fuzzy systems, and artificial neural networks [18]. However, very little work has been done on predicting video QoE, considering the impacts of the fluctuations of wireless network conditions.…”

Section: Qoe Estimation Of Wireless Video Streaming Using Neural Netwmentioning

confidence: 99%

“…To evaluate the video quality, The International Telecommunication Union (ITU) defined a five-level scale, called an ITU-5 point impairment scale, as shown in Table 1. To train the NN, the collection of MOS rankings in compliance with the ITU-T standard [18] during voting periods of human subject experiments was carried out. As presented in [18], there is a close relationship between the MOS, the PSNR, and SSIM, as described in Table 2.…”

Section: Qoe Estimation Of Wireless Video Streaming Using Neural Netwmentioning

confidence: 99%

“…To train the NN, the collection of MOS rankings in compliance with the ITU-T standard [18] during voting periods of human subject experiments was carried out. As presented in [18], there is a close relationship between the MOS, the PSNR, and SSIM, as described in Table 2. After training the NN, the weights and bias parameters in the NN are determined and, then, the closed-form expressions for online prediction of user QoE can be obtained.…”

Section: Qoe Estimation Of Wireless Video Streaming Using Neural Netwmentioning

confidence: 99%

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A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

Chai

Nguyen

2020

Applied Sciences

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This paper presents an innovative approach to develop an advanced 360-degree vision telepresence wheelchair for healthcare applications. The study aims at improving a wide field of view surrounding the wheelchair to provide safe wheelchair navigation and efficient assistance for wheelchair users. A dual-fisheye camera is mounted in front of the wheelchair to capture images which can be then streamed over the Internet. A web real-time communication (WebRTC) protocol was implemented to provide efficient video and data streaming. An estimation model based on artificial neural networks was developed to evaluate the quality of experience (QoE) of video streaming. Experimental results confirmed that the proposed telepresence wheelchair system was able to stream a 360-degree video surrounding the wheelchair smoothly in real-time. The average streaming rate of the entire 360-degree video was 25.83 frames per second (fps), and the average peak signal to noise ratio (PSNR) was 29.06 dB. Simulation results of the proposed QoE estimation scheme provided a prediction accuracy of 94%. Furthermore, the results showed that the designed system could be controlled remotely via the wireless Internet to follow the desired path with high accuracy. The overall results demonstrate the effectiveness of our proposed approach for the 360-degree vision telepresence wheelchair for assistive technology applications.

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“…Valderrama and Gómez [4] chose a different set of inputs, including different lengths of the group of pictures (GOP), two prioritization techniques (DiffServ or BestEffort), and bandwidth bottlenecks in the experimental network. They obtained a Pearson correlation coefficient (PCC) slightly above 0.9, but only one resolution (740 × 480) and a high packet loss rate were used.…”

Section: Related Workmentioning

confidence: 99%

Predicting Perceptual Quality in Internet Television Based on Unsupervised Learning

et al. 2020

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Quality of service (QoS) and quality of experience (QoE) are two major concepts for the quality evaluation of video services. QoS analyzes the technical performance of a network transmission chain (e.g., utilization or packet loss rate). On the other hand, subjective evaluation (QoE) relies on the observer’s opinion, so it cannot provide output in a form of score immediately (extensive time requirements). Although several well-known methods for objective evaluation exist (trying to adopt psychological principles of the human visual system via mathematical models), each of them has its own rating scale without an existing symmetric conversion to a standardized subjective output like MOS (mean opinion score), typically represented by a five-point rating scale. This makes it difficult for network operators to recognize when they have to apply resource reservation control mechanisms. For this reason, we propose an application (classifier) that derivates the subjective end-user quality perception based on a score of objective assessment and selected parameters of each video sequence. Our model integrates the unique benefits of unsupervised learning and clustering techniques such as overfitting avoidance or small dataset requirements. In fact, most of the published papers are based on regression models or supervised clustering. In this article, we also investigate the possibility of a graphical SOM (self-organizing map) representation called a U-matrix as a feature selection method.

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Nonintrusive Method Based on Neural Networks for Video Quality of Experience Assessment

Cited by 6 publications

References 33 publications

Adaptive Reservation of Network Resources According to Video Classification Scenes

Adaptive Reservation of Network Resources According to Video Classification Scenes

A Telepresence Wheelchair with 360-Degree Vision Using WebRTC

Predicting Perceptual Quality in Internet Television Based on Unsupervised Learning

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