Call for Papers—_IEEE Open Journal of Signal Processing_
Special Issue on
*Applied Artificial Intelligence and Machine Learning for Video Coding
and Streaming*
Please note that IEEE Open Journal of Signal Processing (OJSP) is a Gold
Open Access journal, however, _open access fees for papers accepted in
this Special Issue will be waived_; i.e. accepted papers will be
published _free of charge_.
_*Scope*_: Video traffic constituted 75% of all IP traffic in 2017 and
will constitute 82% of all IP traffic by 2022. Improving video co ding
methods and video networking schemes is therefore vital to cope with
this increasing demand. In recent years, we have witnessed how
Artificial Intelligence (AI) and Machine Learning (ML) revolutionized
the field of video coding and streaming as these new solutions now offer
state-of-the-art in many high-level and low-level image and video
related tasks. A range of Convolutional Neural Networks (CNN)-based
video coding tools (rate-distortion optimization, deblocking filters,
interpolation filters, chroma from luma prediction methods), learned
entropy coding, end-to-end image compression techniques, decision trees
based encoder speed ups, fuzzy network bandwidth prediction, and video
network resource allocation via reinforcement learning are among these
efforts. MPEG Ad Hoc Group on deep neural networks based video coding
and JPEG’s learning-based image coding activity, are among recent
initiatives which show that significant effort is required to advance
this field and overcome the existing challenges such a s improving the
compression efficiency, lowering the overhead of computations of these
AI/ML tools, finding suitable loss functions and optimization criteria,
managing network resource according to user experience, accurately
predicting network status, and working towards explainable AI. As both
academic and industry efforts in this direction have been increasing
tremendously, this is the right time to focus on this topic.
For this Special Issue, we invite submissions of papers from the
academia and the industry reporting on the latest scientific and
engineering results and findings in applying AI and ML to video coding
and steaming. Topics of interest include, but are not limited to:
• Towards applicable end-to-end learned image/video compression
• Interpretability and explainability of learned models for video
compression
• Low complexity, energy efficient, and memory efficient AI/ML for
image/video coding
• Distance metrics and optimization criteria for AI/ML-based compression
techniques
• Standard-compliant techniques to integrate AI/ML into video coding and
streaming
• Generative models for video compression and enhancement of compressed
video
• Pre/post processing AI/ML techniques to enhance image/video coding
• AI/ML-based video saliency detection and coding
• AI/ML-based network resource prediction and content adaptation for
video streaming
• AI/ML-based solutions for co-optimization of network and video coding
• Smart network resource allocation for video streaming services
• AI/ML-based video traffic classification and prediction for video
communication
• AI/ML-based video quality assessment, and QoE estimation for video
streaming
• Real-time AI/ML-based video enhancement for efficient video coding
• ML-based packet video network fault detection, isolation, and diagnosis
_*Deadlines*_:
• Initial Paper Submission: December 15, 2020
• Initial Paper Decision: January 30, 2021
• Revised Paper Submission: March 1, 2021
• Final Decision: March 15, 2021
_*Submission*_:
Please follow IEEE OJSP’s paper format and requirements
<https://signalprocessingsociety.org/publications-resources/ieee-open-journal-signal-processing/information-authors-ojsp>.
Once your paper is ready, please submit it via IEEE OJSP’s submission
site https://mc.manuscriptcentral.com/oj-sp, and be sure to select this
Special Issue when submitting. For more information, please contact the
lead Guest Editor Dr. Marta Mrak at [log in to unmask] .
_*Guest Editors*_:
• Marta Mrak, BBC R&D, UK
• Mahmoud Reza Hashemi, University of Tehran, Iran
• Shervin Shirmohammadi, University of Ottawa, Canada
• Ying Chen, Alibaba Cloud Intelligence Group, US
• Moncef Gabbouj, Tampere University, Finland
--
Shervin Shirmohammadi, IEEE Fellow, SM-ACM, Ph.D., PMP, P.Eng.
Professor, University of Ottawa, Canada
Director, DISCOVER Lab
Editor-in-Chief, IEEE Trans. on I&M (TIM)
Associate Editor, ACM Trans. on Multimedia (TOMM)
Steering Committee, IEEE Trans. on Games
http://www.eecs.uottawa.ca/~shervin/
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