Multimedia Communications Using Machine Learning

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 (1 December 2022) | Viewed by 1339

Special Issue Editors

Department of Multimedia and Information and Communication Technology, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: multimedia communication; information communication; computer networks; quality of service (QoS); quality of experience (QoE)
Special Issues, Collections and Topics in MDPI journals
Department of Multimedia and Information and Communication Technology, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: audio and video compression; quality of experience (QoE); TV broadcasting; IP networks
Special Issues, Collections and Topics in MDPI journals
Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: quality of multimedia services; machine learning algorithms; data analysis
Special Issues, Collections and Topics in MDPI journals
Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: audio and video signal processing; functionality and optimization of networks; video quality assessment

Special Issue Information

Dear Colleagues,

The growing interest in real-time services, particularly video transmission over internet protocol (IP) packet networks, has prompted analyses of these services and their behavior. The interest in these networks is intensifying. Video is a major component of all data traffic via IP networks, and its quality has a key role in multimedia technologies.

Communication is a basic connection between people. As people's common communication habits change, so do the requirements for communication and the way care is provided.

Machine learning is revolutionizing the way in which multimedia information is processed and transmitted to users. It is one of the fastest-growing branches of modern science. It is a subfield of artificial intelligence research that concerns the issue of making computers help us solve complex modern problems. The quality of multimedia services can be predicted using machine learning. In addition, the image and video behavior can be modeled such that tremendous compression gains can be achieved.

Thus, machine learning can be employed as a useful tool for several types of multimedia communication systems. It has great potential and helps to achieve a huge increase in performance.

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

  • Computational intelligence algorithms;
  • Quality of Service (QoS) adaptive design and algorithms;
  • Quality of Experience (QoE) adaptive design and algorithms;
  • Mapping quality of service (QoS) to quality of experience (QoE);
  • Very high capacity networks;
  • Computational intelligence optimization;
  • Network optimization and communication protocol;
  • New models of the convolutional neural network;
  • Predictive modeling;
  • Real-time video monitoring, analyzing video content, and detecting incidents;
  • Real-time optimization, planning, and coordination.

Dr. Lukas Sevcik
Dr. Miroslav Uhrina
Dr. Jaroslav Frnda
Dr. Juraj Bienik
Guest Editors

Manuscript Submission Information

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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

  • multimedia communications
  • machine learning
  • neural networks
  • real-time services
  • video coding
  • video quality
  • video streaming
  • image coding

Published Papers (1 paper)

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Research

24 pages, 5712 KiB  
Article
Improved Procedure for Multi-Focus Image Quality Enhancement Using Image Fusion with Rules of Texture Energy Measures in the Hybrid Wavelet Domain
by Chinnem Rama Mohan, Siddavaram Kiran and Vasudeva
Appl. Sci. 2023, 13(4), 2138; https://0-doi-org.brum.beds.ac.uk/10.3390/app13042138 - 07 Feb 2023
Cited by 5 | Viewed by 1066
Abstract
Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional [...] Read more.
Feature extraction is a collection of the necessary detailed information from the given source, which holds the information for further analysis. The quality of the fused image depends on many parameters, particularly its directional selectivity and shift-invariance. On the other hand, the traditional wavelet-based transforms produce ringing distortions and artifacts due to poor directionality and shift invariance. The Dual-Tree Complex Wavelet Transforms (DTCWT) combined with Stationary Wavelet Transform (SWT) as a hybrid wavelet fusion algorithm overcomes the deficiencies of the traditional wavelet-based fusion algorithm and preserves the directional and shift invariance properties. The purpose of SWT is to decompose the given source image into approximate and detailed sub-bands. Further, approximate sub-bands of the given source are decomposed with DTCWT. In this extraction, low-frequency components are considered to implement Texture Energy Measures (TEM), and high-frequency components are considered to implement the absolute-maximum fusion rule. For the detailed sub-bands, the absolute-maximum fusion rule is implemented. The texture energy rules have significantly classified the image and improved the output image’s accuracy after fusion. Finally, inverse SWT is applied to generate an extended fused image. Experimental results are evaluated to show that the proposed approach outperforms approaches reported earlier. This paper proposes a fusion method based on SWT, DTCWT, and TEM to address the inherent defects of both the Parameter Adaptive-Dual Channel Pulse coupled neural network (PA-DCPCNN) and Multiscale Transform-Convolutional Sparse Representation (MST-CSR). Full article
(This article belongs to the Special Issue Multimedia Communications Using Machine Learning)
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