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AI for Video Compression and Its Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 550

Special Issue Editors


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Guest Editor
ARTEMIS Department, Telecom SudParis-Institut Polytechnique de Paris, 91011 Palaiseau, France
Interests: information theory and technologies; image processing; video processing; object detection

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Guest Editor
Cristal Research Laboratory, National School of Computer Sciences, Manouba 2010, Tunisia
Interests: machine learning; pattern recognition geometric computation; PDF estimation; classification

Special Issue Information

Dear Colleagues,

As the traffic for TV programs viewed over the Internet tripled between 2016 and 2021, reaching a monthly total of 42,000 petabytes, and considering that more than 500 hours of content are uploaded every minute on a single video repository, the need for advanced, more effective, and versatile video compression as well as streaming tools has become urgent.

In parallel, AI has become the predilect tool in image and video processing: not only have its results demonstrated their effectiveness in increasing the efficiency of conventional compression, streaming, and video processing tools (indexing, cryptography, and fingerprinting), but they have even resulted in significant paradigm shifts, such as end-to-end video encoding, for instance.

This Special Issue is meant to provide an overview of the emerging trends in video compression and its applications. Topics of interest include, but are not restricted to, AI frameworks for video compression (considered both as an end-to-end application or as a means to increase the effectiveness of conventional codecs) and AI compressed-domain video processing (integrity verification, access control, copyright protection, and content tracking), as well as societal endeavors of such tools and applications.

Dr. Mihai Mitrea
Prof. Dr. Faouzi Ghorbel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI video compression
  • AI-based compressed-domain processing

Published Papers (1 paper)

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Research

13 pages, 6598 KiB  
Article
Region-of-Interest Based Coding Scheme for Live Videos
by Xiuxin Dou, Xixin Cao and Xianguo Zhang
Appl. Sci. 2024, 14(9), 3823; https://doi.org/10.3390/app14093823 - 30 Apr 2024
Viewed by 379
Abstract
In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or [...] Read more.
In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or higher, the transcoding speed of encoders often becomes a limiting factor, leading to streams with substantial bitrates but unsatisfactory quality metrics. To enhance the overall Quality of Service (QoS) without increasing the bitrate, it is essential to improve the quality of Regions of Interest (ROI).Our proposed solution presents an ROI-based rate reservoir model that ingeniously leverages Convolutional Neural Networks (CNNs) to predict rate control parameters. This approach aims to optimize the bitrate allocation within high bitrate live broadcasts, thus enhancing the image quality within ROIs. Experimental outcomes demonstrate that this algorithm manages to increase the bitrate by no more than 5%, effectively redistributing the reduced bitrate across the entire Group of Pictures (GOP). As a result, it ensures a gradual decrease in the quality of Regions of Uninterest (ROU), thereby maintaining a balanced quality experience throughout the broadcasted content. Full article
(This article belongs to the Special Issue AI for Video Compression and Its Applications)
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