scholarly journals Evaluating the Role of Content in Subjective Video Quality Assessment

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Milan Mirkovic ◽  
Petar Vrgovic ◽  
Dubravko Culibrk ◽  
Darko Stefanovic ◽  
Andras Anderla
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Ground Truth ◽  
Quality Score ◽  
Video Quality Assessment ◽  
Video Content

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

Video quality assessment in video streaming services considering user preference for video content

2014 ◽  
Vol 60 (3) ◽  
pp. 436-444 ◽  
Author(s):  
Demóstenes Z. Rodríguez ◽  
Renata L. Rosa ◽  
Eduardo A. Costa ◽  
Julia Abrahão ◽  
Graca Bressan
Keyword(s):  
Quality Assessment ◽  
Video Streaming ◽  
Video Quality ◽  
User Preference ◽  
Video Quality Assessment ◽  
Video Content ◽  
Streaming Services

No-Reference Model for Video Quality Assessment Based on SVM

2013 ◽  
Vol 846-847 ◽  
pp. 1024-1030
Author(s):  
Li Li Wu ◽  
Chun Yan Yu
Keyword(s):  
Quality Assessment ◽  
Packet Loss ◽  
Loss Rate ◽  
Reference Model ◽  
Video Quality ◽  
Packet Loss Rate ◽  
Video Quality Assessment ◽  
Video Content ◽  
Temporal Complexity ◽  
Visual Characteristic

This paper focuses on the video distortion which is caused by the packet loss. Considering the relationship between the human visual perception which is caused by the packet loss and the visual characteristic of the video content, we present a no-reference model for video quality assessment based on Support Vector Machine. The feature vector of the SVM contain temporal complexity, spatial complexity, the average number of bits per frame and the packet loss rate. Temporal complexity, spatial complexity and the average number of bits per frame represent the visual characteristic of the video content. The value of the packet loss rate means the distortion which is caused by the packet loss intuitively. Experimental results show that this model has a good consistency with the subjective.

scholarly journals A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2277
Author(s):  
Chiman Kwan ◽  
Bence Budavari
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Ground Truth ◽  
Video Quality Assessment ◽  
Quality Metric ◽  
Air Turbulence ◽  
Subjective Evaluations ◽  
Assessment Metrics ◽  
Video Quality Metric ◽  
Blind Image

Although many algorithms have been proposed to mitigate air turbulence in optical videos, there do not seem to be consistent blind video quality assessment metrics that can reliably assess different approaches. Blind video quality assessment metrics are necessary because many videos containing air turbulence do not have ground truth. In this paper, a simple and intuitive blind video quality assessment metric is proposed. This metric can reliably and consistently assess various turbulent mitigation algorithms for optical videos. Experimental results using more than 10 videos in the literature show that the proposed metrics correlate well with human subjective evaluations. Compared with an existing blind video metric and two other blind image quality metrics, the proposed metrics performed consistently better.

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