PeQASO: Perceptual Quality Assessment of Streamed Videos Using Optical Flow Features

Aabed, Mohammed A.; AlRegib, Ghassan

doi:10.1109/tbc.2018.2881363

Cited by 4 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper [ 15 ] analyzed the impacts of the compression and network distortion on streaming video. The authors proposed a reduced-reference metric for perceptual video quality evaluation using optical flow functions.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Reservation of Network Resources According to Video Classification Scenes

Sevcik

Vozňák

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The compressed video sequences can suffer from loss of fidelity, compression artefacts, colour fading and blur among others. Considering that video streaming systems aim to stream to the users the highest quality video possible, the accurate evaluation of the visual quality of each compressed version of the video becomes crucial, especially the perceptual quality as experienced by the human visual system (HVS) [4]. Video Quality Assessment (VQA) techniques can be divided into two categories, depending on how the quality evaluation is achieved: (a) Objective VQA approaches, and (b) Subjective VQA approaches.…”

Section: Introductionmentioning

confidence: 99%

A machine-learning-driven solution to the problem of perceptual video quality metrics

Katsigiannis

Rabah

Ramzan

2020

AI for Emerging Verticals: Human-Robot Computing, Sensing and Networking

View full text Add to dashboard Cite

show abstract

A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos

Xian

Zhou

Fang

2022

Information Sciences

View full text Add to dashboard Cite

PeQASO: Perceptual Quality Assessment of Streamed Videos Using Optical Flow Features

Cited by 4 publications

References 60 publications

Adaptive Reservation of Network Resources According to Video Classification Scenes

Adaptive Reservation of Network Resources According to Video Classification Scenes

A machine-learning-driven solution to the problem of perceptual video quality metrics

A content-oriented no-reference perceptual video quality assessment method for computer graphics animation videos

Contact Info

Product

Resources

About