loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Akm Ashiquzzaman 1 ; Sung Min Oh 1 ; Dongsu Lee 1 ; Hoehyeong Jung 1 ; Tai-won Um 2 and Jinsul Kim 1

Affiliations: 1 School of Electronics and Computer Engineering, Chonnam National University, Gwangju and South Korea ; 2 Department of Information and Communication Engineering, Chosun University, Gwangju and South Korea

Keyword(s): Deep Learning, Convolutional Neural Network, Computer Networks, Video Steaming, 4K UHD, QoE.

Related Ontology Subjects/Areas/Topics: Application Domains ; Computer Simulation Techniques ; Formal Methods ; Neural Nets and Fuzzy Systems ; Simulation and Modeling ; Simulation Tools and Platforms ; Telecommunication Systems and Networks

Abstract: With the rapid development of modern high resolution video streaming services, providing high Quality of Experience (QoE) has become a crucial service for any media streaming platforms. Most often it is necessary of provide the QoE with NR-IQA, which is a daunting task for any present network system for it’s huge computational overloads and often inaccurate results. So in this research paper a new type of this NR-IQA was proposed that resolves these issues. In this work we have described a deep-learning based Convolutional Neural Network (CNN) to accurately predict image quality without a reference image. This model processes the RAW RGB pixel images as input, the CNN works in the spatial domain without using any hand-crafted or derived features that are employed by most previous methods. The proposed CNN is utilized to classify all images in a MOS category. This approach achieves state of the art performance on the KoniQ-10k dataset and shows excellent generalization ability in clas sifying proper images into proper category. Detailed processing on images with data augmentation revealed the high quality estimation and classifying ability of our CNN, which is a novel system by far in these field. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.24.20.141

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ashiquzzaman, A.; Oh, S.; Lee, D.; Jung, H.; Um, T. and Kim, J. (2019). Deeplearning Convolutional Neural Network based QoE Assessment Module for 4K UHD Video Streaming. In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-381-0; ISSN 2184-2841, SciTePress, pages 392-397. DOI: 10.5220/0008117903920397

@conference{simultech19,
author={Akm Ashiquzzaman. and Sung Min Oh. and Dongsu Lee. and Hoehyeong Jung. and Tai{-}won Um. and Jinsul Kim.},
title={Deeplearning Convolutional Neural Network based QoE Assessment Module for 4K UHD Video Streaming},
booktitle={Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2019},
pages={392-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008117903920397},
isbn={978-989-758-381-0},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Deeplearning Convolutional Neural Network based QoE Assessment Module for 4K UHD Video Streaming
SN - 978-989-758-381-0
IS - 2184-2841
AU - Ashiquzzaman, A.
AU - Oh, S.
AU - Lee, D.
AU - Jung, H.
AU - Um, T.
AU - Kim, J.
PY - 2019
SP - 392
EP - 397
DO - 10.5220/0008117903920397
PB - SciTePress