ISSN: 1690-4524 (Online)
Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Indexed byDOAJ (Directory of Open Access Journals)Benefits of supplying DOAJ with metadata:DOAJ's statistics show more than 900 000 page views and 300 000 unique visitors a month to DOAJ from all over the world. Many aggregators, databases, libraries, publishers and search portals collect our free metadata and include it in their products. Examples are Scopus, Serial Solutions and EBSCO . DOAJ is OAI compliant and once an article is in DOAJ, it is automatically harvestable. DOAJ is OpenURL compliant and once an article is in DOAJ, it is automatically linkable. Over 95% of the DOAJ Publisher community said that DOAJ is important for increasing their journal's visibility. DOAJ is often cited as a source of quality, open access journals in research and scholarly publishing circles. JSCI Supplies DOAJ with Meta Data
, Academic Journals Database, and Google Scholar
Listed inCabell Directory of Publishing Opportunities and in Ulrich’s Periodical Directory
Published by
The International Institute of Informatics and Cybernetics
Re-Published in
Academia.edu (A Community of about 40.000.000 Academics)
Quantitative Endosurgery Process Analysis by Machine Learning Method Bojan Nokovic , Andrew Lambe (pages: 1-7) Modelling Student Performance in a Structural Steel Graduate-Based Module: A Comparative Analysis Between K-Nearest Neighbor and Dummy Classifiers Masengo Ilunga , Omphemetse Zimbili , Phahlani Mampilo , Agarwal Abhishek (pages: 8-15) Interoperable Digital Skills for Foreign Languages Education in the COVID-19 Paradigm Rusudan Makhachashvili , Ivan Semenist , Iryna Vorotnykova (pages: 16-20) Education, Training and Informatics Go Hand in Hand in (Foreign) Higher Education Institutions (HEIs) – Case Studies From Live and Online Classrooms Ekaterini Nikolarea (pages: 21-29) Enhancing Pedagogical and Digital Competencies Through Digital Tools: A Proposal for Semi-schooled Language Teaching Programs in Oaxaca, Mexico José de Jesús Bautista Hernández , Eduardo Bustos Farías , Norma Patricia Maldonado Reynoso (pages: 30-35) Railway Track Degradation Modelling Using Finite Element Analysis: A Case Study in South Africa Ntombela Lunga , Masengo Ilunga (pages: 36-50) Continuum of Academic Collaboration: Issues of Inconsistent Terminology in Multilingual Context Cristo Leon , James Lipuma , Marcos O. Cabobianco , Maria B. Daizo (pages: 51-62) Peat Resource Management and Climate Change Mitigation Issues – Case of Latvia Anita Titova , Natalja Lace (pages: 63-70) Using Geospatial Computation Intelligence for Mapping Temporal Evolution of Urban Built-up in Selected Areas of the Ekurhuleni Municipality, South Africa Jo-Anne Correia , Masengo Ilunga (pages: 71-80) Cybernetics and Informatics of Generative AI for Transdisciplinary Communication in Education Rusudan Makhachashvili , Ivan Semenist (pages: 81-88) Navigating Psychological Riptides: How Seafarers Cope and Seek Help for Mental Health Needs Coleen Abadicio , Stella Louise Arenas , Rosette Renee Hahn , Angel Berry Maleriado , Ramon Miguel Mariano , Rodolfo Antonio Ma. Zabella , Genejane Adarlo (pages: 89-98)
ABSTRACT
Building a Reduced Reference Video Quality Metric with Very Low Overhead Using Multivariate Data Analysis Tobias Oelbaum, Klaus Diepold
In this contribution a reduced reference video quality
metric for AVC/H.264 is proposed that needs only a very low
overhead (not more than two bytes per sequence). This reduced
reference metric uses well established algorithms to measure objective
features of the video such as ’blur’ or ’blocking’. Those
measurements are then combined into a single measurement for
the overall video quality. The weights of the single features and the
combination of those are determined using methods provided by
multivariate data analysis. The proposed metric is verified using
a data set of AVC/H.264 encoded videos and the corresponding
results of a carefully designed and conducted subjective evaluation.
Results show that the proposed reduced reference metric not only
outperforms standard PSNR but also two well known full reference
metrics.
Full Text