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Projecte matriculat

Títol: Analysis and Evaluation of No-Reference Video Quality Assesment using Neural Networks

Director: TARRÉS RUIZ, Francesc

Departament: TSC

Títol: Analysis and Evaluation of No-Reference Video Quality Assesment using Neural Networks

Data inici oferta: 07-01-2020     Data finalització oferta: 07-09-2020


Estudis d'assignació del projecte:
    GR ENG SIS TELECOMUN
Tipus: Individual
 
Lloc de realització: EETAC
 
Paraules clau:
Video analysis, Video Quality Assesment, Deep Learning, Python, Machine Learning
 
Descripció del contingut i pla d'activitats:
The measure of the subjective quality of video is a difficult task that requires the
preparation of a large series of testings defining examples, groups of people, control
groups, etc. Therefore, some video analysis techniques have been developed in order to
predict the 'subjective' quality of a video. These techniques a divided in 2 groups:
reference and no-reference VQA algortihms, depending of the requirement of the original
video in the prediction algorithm. When the original video is not available the problem
becomes specially complex but it is of great interest in many applications.

In this thesis we will explore some strategies based on using deep learning in order to
obtain Video Quality Assessment without a Reference Signal. The main approach will be
to develope a neural architecture that will be trained using well known VQA methods such
as VMAF or other methods. Once trained, the system will have to predict the quality of
new videos without reference
 
Orientació a l'estudiant:
Image Processing, Python, Machine Learning, Deep Learning, Keras
 
 
 
Horari d'atenció a estudiants per a l'assignació de projecte:

Data de generació 04/03/2021