AI for Multimedia Information Processing

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

Deadline for manuscript submissions: 10 June 2024 | Viewed by 1039

Special Issue Editor


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Guest Editor
Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Interests: audio; broadcasting; coding; compression; digitization; mobile technologies; multimedia; positioning; signal processing; speech processing; video; wireless communication
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Special Issue Information

Dear Colleagues,

Artificial Intelligence is applied all around us in our modern-day, digitalized society. From consumer behavior analysis to Internet profiling, it accompanies us anytime and everywhere, regardless of the type of device that we utilize. All online activities, including web browsing, streaming, shopping, or downloading apps, leave traces of our interests and preferences, which can be used to train various tools. Whether you prefer desktop or mobile devices, AI-related technologies can enhance the pictures that you take, sharpen and stabilize recorded videos, introduce effects or delete unwanted elements. These solutions can also denoise your voice calls, finetune prerecorded audio material, and even imitate the timbre of a celebrity. When it comes to multimedia information processing, the sky seems to be the only limit.

However, there are still numerous fields in which AI technologies could help to raise the quality of life of individuals. These could include voice or video assistants for the youngest or elderly, as well as pedestrial or motorized navigation. With the aid of modern tools, we could speed up medical diagnostics, design personalized therapy or employee training courses, etc.

In this Special Issue, we invite the scientific community to publish works focused on AI for multimedia information processing.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • AI tools and software;
  • Audio and video signal processing;
  • Coding and compression;
  • Content creation and enhancement;
  • Mobile and desktop technologies;
  • Multimedia digitization;
  • Quality of life.

I look forward to receiving your contributions.

Dr. Przemysław Falkowski-Gilski
Guest Editor

Manuscript Submission Information

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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 tools and software
  • audio signal processing
  • coding and compression
  • content creation
  • content enhancement
  • desktop technologies
  • digitization
  • mobile technologies
  • multimedia
  • quality of life
  • video signal processing

Published Papers (1 paper)

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Research

18 pages, 457 KiB  
Article
Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
by Arkadiusz Biernacki
Appl. Sci. 2024, 14(5), 1962; https://doi.org/10.3390/app14051962 - 28 Feb 2024
Viewed by 639
Abstract
Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means [...] Read more.
Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace. Full article
(This article belongs to the Special Issue AI for Multimedia Information Processing)
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