Open AccessA Multi-parameter Video Quality Assessment Model Based on 3D Convolutional Neural Network on the Cloud
Author(s) -
Xue Li,
Jiali Qiu
Publication year - 2021
Publication title -
asp transactions on internet of things
Language(s) - English
Resource type - Journals
ISSN - 2788-8401
DOI - 10.52810/tiot.2021.100063
Subject(s) - computer science , video quality , convolutional neural network , cloud computing , upload , codec , data deduplication , network packet , real time computing , packet loss , computer network , data mining , artificial intelligence , database , operating system , metric (unit) , operations management , economics
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.