NDLI: No-reference video quality assessment method based on spatio-temporal features using the ELM algorithm

Content Provider	IET Digital Library
Author	Silva, Wyllian Bezerra da Mikowski, Alexandre Casali, Rafael Machado
Abstract	This work presents an application of the extreme learning machine (ELM) algorithm based on a single-hidden layer feedforward neural network for no-reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no-reference, Joint Photographic Experts Group No-Reference, metric and full-reference metrics, for instance, peak signal-to-noise ratio, structural similarity (SSIM) and multi-scale-SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.
Starting Page	1316
Ending Page	1326
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 7, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0941
Journal	IET Image Processing
Publisher Date	2020-04-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Data Compression ELM Algorithm Extreme Learning Machine Algorithm Feature Extraction Feedforward Neural Network Full-reference Metrics Image And Video Coding Joint Photographic Experts Group No-reference Knowledge Engineering Technique Learning in AI LIVE Video Data Base Neural Computing Technique No-reference Video Quality Assessment Pearson Correlation Coefficient Quality Monitoring Reception System Reference Video Quality Assessment Method Single-hidden Layer Feedforward Neural Network Spatio-temporal Feature Spearman Correlation Coefficient Stop Criteria Video Coding Video Signal Processing Video Transmission
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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