A video quality assessment model based on the MPEG-7 descriptor

2011 ◽  
Author(s):  
Masaharu Sato ◽  
Yuukou Horita
Keyword(s):  
Quality Assessment ◽  
Video Quality ◽  
Assessment Model ◽  
Video Quality Assessment ◽  
Model Based

A Video Quality Assessment Model Based on Edge Information

2012 ◽  
Vol 263-266 ◽  
pp. 218-222
Author(s):  
Xue Hui Wei ◽  
Meng Zhao
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Video Quality ◽  
Assessment Model ◽  
Video Quality Assessment ◽  
Multilinear Regression ◽  
Quality Estimation ◽  
Edge Information ◽  
Model Based ◽  
Proposed Model

Video quality assessment can be gotten by combination the distortion in the space area and the time area. As we all known that edge information is one of the most important features in image quality estimation. Based on the edge model in the perceived image quality estimation, we used it in the space and time area in the video, and get the edge information distorted model in space area and time area of the video. Using multilinear regression to combine the two models, we can get the video quality assessment model based on edge information. The proposed model only uses the edge information, and the consumption in both areas is small. After compared with other methods given by video quality estimation group, it’s found that our method is convenient and good at the 50 Hz video sequence of low bit rate(768kb/s -4.5 Mb/s).

scholarly journals A Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud

2021 ◽  
Vol 1 (2) ◽  
pp. 14-22
Author(s):  
Xue Li ◽  
Jiali Qiu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Packet Loss ◽  
Video Quality ◽  
Assessment Model ◽  
Video Quality Assessment ◽  
Network Bandwidth ◽  
Model Based ◽  
The Impact

As the rapid development of big data and the artificial intelligence technology, users prefer uploading more and more local files to the cloud server to reduce the pressure of local storage, but when users upload more and more duplicate files , not only wasting the network bandwidth, but also bringing much more inconvenience to the server management, especially images and videos. To solve the problems above, we design a multi-parameter video quality assessment model based on 3D convolutional neural network in the video deduplication system, we use a method similar to analytic hierarchy process to comprehensively evaluate the impact of packet loss rate, codec, frame rate, bit rate, resolution on video quality, and build a two-stream 3D convolutional neural network from the spatial flow and timing flow to capture the details of video distortion, set the coding layer to remove redundant distortion information. Finally, the LIVE and CSIQ data sets are used for experimental verification, we compare the performance of the proposed scheme with the V-BLIINDS scheme and VIDEO scheme under different packet loss rates. We also use the part of data set to simulate the interaction process between the client and the server, then test the time cost of the scheme. On the whole, the scheme proposed in this paper has a high quality assessment efficiency.

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