Special Issue Information Special Issue Call for Paper Other Special Issues on this journal Closed Special Issues
Data-driven Personalisation of Television Content

Data-driven Personalisation of Television Content

Journal
Impact Score 1.81

OFFICIAL WEBSITE

Special Issue Information

Submission Deadline: 15-09-2021
Journal Impact Score: 1.81
Journal Name: Multimedia Systems
Publisher: Multimedia Systems
Journal & Submission Website: https://www.springer.com/journal/530

Special Issue Call for Papers

Guest Editors

Lyndon J B Nixon, MODUL University, Austria ([email protected])

Jeremy Foss, Birmingham City University, UK ([email protected])

Vasileios Mezaris, Centre for Research and Technology Hellas, Greece ([email protected])

Aims and scope

Television content is no longer consumed only via traditional, linear TV broadcasting. In fact, recent surveys have shown that 6 out of 10 people would rather watch online videos than television, and 78% of people do watch online videos every week. Legacy content creation and distribution workflows need to adapt to multi-channel publication of individually personalised media assets.

The aim of this Special Issue is to address the increasing importance and relevance of richly granular and semantically expressive data about TV and immersive audiovisual content in the media value chain. Such data needs software, specifications, standards and best practices for extraction, modelling and management before it can be meaningfully reused in new, innovative services for TV or other immersive audiovisual settings (e.g. 360° video in AR or MR).

The topics of interest of the Special Issue include, but are not limited to:

Important dates

Manuscript submission deadline: September 15, 2021

Decision notification: November 30, 2021

Author revisions due (if applicable): January 15, 2022

Final decision notification: February 15, 2022

Submission Guidelines

Submit manuscripts to: http://MMSJ.edmgr.com. Select the title of the special issue as the article type or when asked if the article is for a special issue.

Papers submitted to this special issue must be original and must not be under consideration for publication in any other journal or conference.

Extensions of previously-published work may be submitted only if the new submission introduces substantially new content.

The manuscripts will be peer-reviewed strictly following the reviewing procedures.

All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers.

The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references).

Authors should prepare their manuscript according to the journal's Submission Guidelines at https://www.springer.com/journal/530 

Closed Special Issues

Publisher
Journal Details
Closing date
G2R Score
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-11-2021 G2R Score: 1.81
Few-shot Learning for Intelligent Multimedia Systems

Few-shot Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-11-2021 G2R Score: 1.81
Trustworthy Multimedia Big Data Computing

Trustworthy Multimedia Big Data Computing

Multimedia Systems
Closing date: 30-10-2021 G2R Score: 1.81
Data-driven Personalisation of Television Content

Data-driven Personalisation of Television Content

Multimedia Systems
Closing date: 15-09-2021 G2R Score: 1.81
Data-driven Personalisation of Television Content

Data-driven Personalisation of Television Content

Multimedia Systems
Closing date: 15-09-2021 G2R Score: 1.81
Deep Learning for Multimedia Healthcare

Deep Learning for Multimedia Healthcare

Multimedia Systems
Closing date: 15-12-2020 G2R Score: 1.81
Deep Learning for Intelligent Multimedia Systems

Deep Learning for Intelligent Multimedia Systems

Multimedia Systems
Closing date: 15-10-2020 G2R Score: 1.81
Deep learning methods for cyber bullying detection in multi-modal data

Deep learning methods for cyber bullying detection in multi-modal data

Multimedia Systems
Closing date: 30-07-2020 G2R Score: 1.81
Deep Learning for Emerging Big Multimedia Super-Resolution

Deep Learning for Emerging Big Multimedia Super-Resolution

Multimedia Systems
Closing date: 15-07-2020 G2R Score: 1.81
Low complexity methods for multimedia security

Low complexity methods for multimedia security

Multimedia Systems
Closing date: 15-06-2020 G2R Score: 1.81