Abstract
To assess the real-time transmission video’s quality, this paper persents a approach which used FR video quality assessment (VQA) model to satisfy the objective and subjective measurement requirement. If we want to get the reference video in the measuring terminal and to make a assessment, there are two problems which are how to certain the reference video frame and how to make the objective score close to the subject assessment. We present in this paper a novel method of computing the order number of the video frame in the test point. In order to establish the relationship between the objective distortion and the subjective score, we used the “best-fit” regressed curve model and the BP neural network to describe prediction formula. This work is the mainly aim to get the high accurency assessment results with the human subjective feeling. So we select huge video sources for testing and training. The experimental results show that the proposed approach is suit to assess the video quality using FR model and the converted subjective score is available.
Highlights
Video quality assessment (VQA) plays improtant role in various video communication applications, while the demand for video applications into everybody’s life are rapidly growing
A total 150 video sequences are generated from 10 reference video sequences download from VQEG [13]-[14] and LIVE database [15]
This paper has proposed a novel method to calculate the order number of the reference video in receiving end by adding special mark in marcoblocks
Summary
Abstract—To assess the real-time transmission video’s quality, this paper persents a approach which used FR video quality assessment (VQA) model to satisfy the objective and subjective measurement requirement. If we want to get the reference video in the measuring terminal and to make a assessment, there are two problems which are how to certain the reference video frame and how to make the objective score close to the subject assessment. We present in this paper a novel method of computing the order number of the video frame in the test point. In order to establish the relationship between the objective distortion and the subjective score, we used the “best-fit” regressed curve model and the BP neural network to describe prediction formula. The experimental results show that the proposed approach is suit to assess the video quality using FR model and the converted subjective score is available
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More From: International Journal of Advanced Computer Science and Applications
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