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Half reference video QoE objective evaluation method based on image feature information

A semi-reference, video technology, applied in the field of communication, can solve problems such as difficult application, no evaluation accuracy test, full reference method or no reference method practicability limitations, etc., to achieve the effect of speeding up processing, easy deployment and implementation

Inactive Publication Date: 2014-06-04
BEIJING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Most of the existing QoE objective evaluation methods are full-reference or no-reference methods. The full-reference method can get the most accurate evaluation results, but it is difficult to apply; the no-reference method is easy to deploy, but usually only applies to specific damage scenarios; the semi-reference method Although a good balance can be achieved between the two, there is a lack of mature solutions
[0004] The problem of the existing technology is that both the full-reference method and the no-reference method have limitations in practicability, and the existing technology does not have a horizontal comparison result for testing the accuracy of the evaluation

Method used

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  • Half reference video QoE objective evaluation method based on image feature information
  • Half reference video QoE objective evaluation method based on image feature information
  • Half reference video QoE objective evaluation method based on image feature information

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Embodiment 1

[0029] This embodiment discloses a semi-reference video QoE objective evaluation method based on image feature information, the method comprising:

[0030] The operator side extracts the saliency information map and the texture information map of each frame image in the original video, compresses and processes the saliency information map and the texture information map, and obtains semi-reference data of the original video;

[0031] The user terminal receives the semi-reference data of the original video and the damaged video from the operator, extracts the saliency information map and texture information map of each frame of the damaged video, and obtains the semi-reference data of the damaged video. Semi-reference data of video and damaged video, calculate the damage of the damaged video, and use the pre-trained neural network algorithm to evaluate the subjective quality MOS, wherein the damaged video is the operator’s data transmitted through the lossy channel end of the o...

Embodiment 2

[0047] This embodiment discloses a semi-reference video QoE objective evaluation method based on image feature information, such as figure 1 As shown, the present invention carries out semi-reference QoE objective quality assessment method to real-time video service and mainly is divided into 11 steps, can be divided into operator's end and user's end two parts, wherein operator's end comprises 5 steps, and user's end comprises 6 steps step. Its overall flow chart is as follows figure 1 As shown, each step is described below.

[0048] Operator side:

[0049] 101) Extract saliency information for each frame in the original video separately. Saliency describes relatively more attention-grabbing regions in an image. First, the saliency components are constructed from four aspects of intensity, color, direction and skin tone, and then the 4 components are combined into a saliency map according to different weights. Among them, before calculating the salience component of skin...

Embodiment 3

[0107] On the public video subjective quality database LIVE Video Quality Database (LVQD), the accuracy of the evaluation of the present invention is tested, and the test results are as follows: figure 2 shown. LVQD contains 10 groups of videos, each group contains 1 original (lossless) video, and 15 damaged videos constructed from the perspectives of H.264 compression damage, MPEG2 compression damage, IP transmission damage and wireless transmission damage, so the entire database A total of 150 damaged videos are included. For each damaged video, a subjective score was made according to the ITU-R BT.500-11 standard. Subjective scoring uses a single-stimulus process, with raters giving scores on a continuous scale from 0 to 100. A total of 38 raters were invited for the experiment, and 9 of them were judged to be invalid according to the criteria and were eliminated. After the remaining 29 scores were processed according to the standard, the MOS and score variance correspo...

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Abstract

The invention discloses a half reference video QoE objective evaluation method based on image feature information. The method includes the steps that a significance information graph and a texture information graph of each frame of image in an original video are extracted by an operator terminal, the significance information graphs and the texture information graphs are compressed, and half reference data of the original video are obtained; the half reference data of the original video and a damaged video are transmitted from the operator terminal and then are received by a user terminal, a significance information graph and a texture information graph of each frame of image in the damaged video are extracted, half reference data of the damaged video are obtained, the damaged condition of the damaged video is calculated according to the half reference data of the original video and the half reference data of the damaged video, and the subjective perception quality MOS is evaluated by using a neural network algorithm which is well trained in advance.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a semi-reference video QoE objective evaluation method based on image feature information. Background technique [0002] With the popularity of wireless networks and high-speed broadband access, real-time video services are experiencing rapid development. QoE (Quality of Experience) indicators can reflect the service quality of real-time video services. The objective method of QoE quality evaluation for real-time video services (also known as the QoE objective evaluation method) is to evaluate subjective scores based on specific objective service quality indicators. QoE objective evaluation methods can be divided into three categories according to the usage of original video data, namely full reference (requires all original data), semi-reference (requires part of the original data) and no reference (requires no original data). [0003] Most of the existing QoE objective ...

Claims

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Application Information

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IPC IPC(8): H04N17/00G06T7/00
Inventor 李文璟喻鹏罗千耿杨嵇华
Owner BEIJING UNIV OF POSTS & TELECOMM
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