Objective Video Quality Assessment Based on Neural Networks | Zendy

Diego P. A. Menor | Zendy; C.A.B. Mello | Zendy; Cleber Zanchettin | Zendy

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Objective Video Quality Assessment Based on Neural Networks

Author(s) -

Diego P. A. Menor,

C.A.B. Mello,

Cleber Zanchettin

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.08.202

Subject(s) - computer science , video quality , subjective video quality , quality of experience , artificial neural network , artificial intelligence , distortion (music) , pevq , quality (philosophy) , image quality , video processing , quality assessment , computer vision , image (mathematics) , data mining , evaluation methods , quality of service , telecommunications , metric (unit) , amplifier , operations management , philosophy , engineering , bandwidth (computing) , epistemology , reliability engineering , economics

Image/Video Quality Assessment (IQA/VQA) plays a significant role in image and video processing, as it can directly predict the impact of distortions on the video in the quality of experience (QoE) of the user. For this propose, in this paper, it is presented a new method for objective video quality assessment using an artificial neural network to predict the subjective evaluation of the video as if it were observed by a human user. The network was trained using degradation indicators extracted from the VQEG Phase I video database, which describe the level of distortion suffered by the original video under spatial and temporal scopes. The proposed method obtained an excellent correlation with the subjective scores over this same database

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