Recently, the multimedia landscape underwent a revolution around several technological innovations. Although these new innovations are not massively adopted on the market yet, they show a promising perspective on the future of multimedia consumption. These emerging multimedia technologies lead to a plethora of new questions about compression, transmission, perception, and finally QoE.
The advent of machine learning, especially deep learning and AI, coeval with large scale of media, has impacted on various aspects of our research and applications. The advantageous but typically unstructured large amount of multimedia content comes from an assortment of sources, in various modalities, and offer diverse levels of knowledge. It gives rise to a great multimedia challenge that concerns not only the fusion of multi-source multimedia data, but also the needs to offer insights, tackle real-word problems and solutions, and serve the intended users and communities
These types of challenges have resulted in a far-reaching surge of interest in AI. This is mainly due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. In all such applications, it is of paramount importance to understand, trust, and in one word “explain” the rationale behind AI models’ decisions. Although different efforts have been initiated recently to explain behaviour and decisions of these models through Explainable AI, which aims at reasoning about the behaviour and decisions, is still in its inception in the field of multimedia.
This special issue will provide new insights, tools and technologies specific for Explainable AI on emerging multimedia technologies
Topics of interest:
Explainable AI in multimedia compression, transmission, and perception
Explainable AI in multimedia content retrieval, personalization and recommendation
Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models
Responsible artificial intelligence: designing AI for human values
Architectures, algorithms and tools to support explainability
Reconfigurable processor for deep learning in multimedia technologies
Explainable AI applications and practices
Explainable AI for Quality of Experience (QoE) of emerging media, including omnidirectional video (360° video), light field imaging, and High Dynamic Range (HDR) video.
Explainable AI for user behavior and viewing behavior when interacting emerging media technologies.
Explainable AI Perceptual analysis and models for emerging media technologies.
Explainable AI Objective metrics for emerging media innovations.
Prospective authors are invited to submit high-quality research papers that have not already been submitted, accepted or published. The manuscript should be prepared according to the journal’s author guidelines and submitted through the online system. All submitted manuscripts will be reviewed and evaluated based on their relevance, originality, novelty, and presentation. Please find the full detailed information at: https://www.journals.elsevier.com/signal-processing-image-communication
Please choose article type name “Explainable AI” when submitting to this special issue
Manuscript submission deadline: June 1, 2020
First reviews completed deadline: October 1, 2020
Revised manuscripts deadline: November 1, 2020
Final acceptance deadline: December 1, 2020
Expected publication date: February 1, 2021
Dr Naeem Ramzan
Professor, University of the West of Scotland, UK
Dr Hassan RABAH
Professor, University of Lorraine, France