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
Transfer Learning for Facial Emotion Recognition on Small Datasets Paolo Barile , Clara Bassano , Paolo Piciocchi (pages: 1-5) How to Link Educational Purposes and Immersive Video Games Development? An Ontological Approach Proposal Nathan Aky (pages: 6-13) Application of Building Information Modeling (BIM) in the Planning and Construction of a Building Renata Maria Abrantes Baracho , Luiz Gustavo da Silva Santiago , Antonio Tagore Assumpção Mendoza e Silva , Marcelo Franco Porto (pages: 14-19) Transformative, Transdisciplinary, Transcendent Digital Education: Synergy, Sustainability and Calamity Rusudan Makhachashvili , Ivan Semenist (pages: 20-27) New Online Tools for the Data Visualization of Bivalve Molluscs' Production Areas of Veneto Region Eleonora Franzago , Claudia Casarotto , Matteo Trolese , Marica Toson , Mirko Ruzza , Manuela Dalla Pozza , Grazia Manca , Giuseppe Arcangeli , Nicola Ferrè , Laura Bille (pages: 28-32) Geodata Processing Methodology on GIS Platforms When Creating Spatial Development Plans of Territorial Communities: Case of Ukraine Olena Kopishynska , Yurii Utkin , Ihor Sliusar , Leonid Flehantov , Mykola Somych , Oksana Yakovlieva , Olena Scryl (pages: 33-40) D-CIDE: An Interactive Code Learning Program Lukas Grant , Matthew F. Tennyson , Jason Owen (pages: 41-46) Interdisciplinary Digital Skills Development for Educational Communication: Emergency and Ai-Enhanced Digitization Rusudan Makhachashvili , Ivan Semenist , Ganna Prihodko , Irina Kolegaeva , Olexandra Prykhodchenko , Olena Tupakhina (pages: 47-51) Interdisciplinarity in Smart Systems Applied to Rural School Transport in Brazil Renata Maria Abrantes Baracho , Mozart Joaquim Magalhães Vidigal , Marcelo Franco Porto , Beatriz Couto (pages: 52-59) Peculiarities of the Realization of IT Projects for the Implementation of ERP Systems on the Path of Digitalization of Territorial Communities Activities Olena Kopishynska , Yurii Utkin , Ihor Sliusar , Khanlar Makhmudov , Olena Kalashnyk , Svitlana Moroz , Olena Kyrychenko (pages: 60-67)
ABSTRACT
Building a Reduced Reference Video Quality Metric with Very Low Overhead Using Multivariate Data Analysis
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
