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Electronics
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Video Coding, Processing, and Delivery for Future Applications
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Video Coding, Processing, and Delivery for Future Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 9263

Special Issue Editors

Dr. Maria Koziri

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Guest Editor
Department of Computer Science and Telecommunications, University of Thessaly, Lamia, Greece
Interests: video coding; video transcoding; parallel processing; image processing
Dr. Panagiotis Oikonomou

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, South University of Science and Technology of China, Shenzhen, China
Interests: cloud Computing; scheduling algorithms; video transcoding; heuristic algorithms
Prof. Dr. Ioannis Anagnostopoulos

E-Mail Website
Guest Editor
Department of Computer Science and Biomedical Informatics, University of Thessaly, 351-00 Lamia, Greece
Interests: internet technologies; health information systems; data management in bioinformatics; semantic interoperability; linked data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last two years, the COVID-19 pandemic has turned social distancing into the new normal, with numerous social activities such as work, education, and even friend and family gatherings taking place—to a large extent—virtually. As video is an integral part of application used for teleconferencing, teleworking, tele-education, etc., it constitutes about three-quarters of Internet and mobile network traffic. This trend will likely continue as the increased use of applications ranging from autonomous cars, smart cities, and surveillance to factory automation, smart retailing, and many others make imperative the need for new techniques and standards to improve performance and ease implementation of machine-to-machine (M2M) video processing and transmission.

To cope with an everlasting demand for video transmission, new video standards and video compression algorithms are essential. The latter can exploit new computing paradigms such as cloud computing and mobile edge computing for faster and more efficient video delivery and video creation, especially for M2M video applications.

The aim of this Special Issue is to collect the experiences of leading scientists of the field, but also to be an assessment tool for people who are new to the world of video coding and processing.

This Special Issue intends to cover the following topics, but is not limited to them:

  • Video coding for new generation video standards (e.g., VVC, AV1);
  • Next-generation video codecs (e.g., VCM);
  • Video transcoding;
  • Machine learning in video coding;
  • Complexity reduction techniques for video coding standards;
  • Mobile edge computing for media;
  • Cloud computing for media;
  • Video-related IoT;
  • Hardware implementation for media;
  • Parallel media processing.

Dr. Maria Koziri
Dr. Panagiotis Oikonomou
Prof. Dr. Ioannis Anagnostopoulos
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. Electronics 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

  • video coding
  • machine learning
  • video transcoding
  • parallel processing
  • cloud computing
  • IoT
  • hardware acceleration

Published Papers (5 papers)

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Research

23 pages, 1746 KiB  
Article
Machine Learning Based Fast QTMTT Partitioning Strategy for VVenC Encoder in Intra Coding
by Ibrahim Taabane, Daniel Menard, Anass Mansouri and Ali Ahaitouf
Electronics 2023, 12(6), 1338; https://doi.org/10.3390/electronics12061338 - 11 Mar 2023
Cited by 4 | Viewed by 1988
Abstract
The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). [...] Read more.
The newest video compression standard, Versatile Video Coding (VVC), was finalized in July 2020 by the Joint Video Experts Team (JVET). Its main goal is to reduce the bitrate by 50% over its predecessor video coding standard, the High Efficiency Video Coding (HEVC). Due to the new advanced tools and features included in VVC, it actually provides high coding performances—for instance, the Quad Tree with nested Multi-Type Tree (QTMTT) involved in the partitioning block. Furthermore, VVC introduces various techniques that allow for superior performance compared to HEVC, but with an increase in the computational complexity. To tackle this complexity, a fast Coding Unit partition algorithm based on machine learning for the intra configuration in VVC is proposed in this work. The proposed algorithm is formed by five binary Light Gradient Boosting Machine (LightGBM) classifiers, which can directly predict the most probable split mode for each coding unit without passing through the exhaustive process known as Rate Distortion Optimization (RDO). These LightGBM classifiers were offline trained on a large dataset; then, they were embedded on the optimized implementation of VVC known as VVenC. The results of our experiment show that our proposed approach has good trade-offs in terms of time-saving and coding efficiency. Depending on the preset chosen, our approach achieves an average time savings of 30.21% to 82.46% compared to the VVenC encoder anchor, and a Bjøntegaard Delta Bitrate (BDBR) increase of 0.67% to 3.01%, respectively. Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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22 pages, 5025 KiB  
Article
Rate Control Technology for Next Generation Video Coding Overview and Future Perspective
by Hao Zeng, Jun Xu, Shuqian He, Zhengjie Deng and Chun Shi
Electronics 2022, 11(23), 4052; https://doi.org/10.3390/electronics11234052 - 06 Dec 2022
Cited by 4 | Viewed by 2249
Abstract
Video data have become the main data traffic on the Internet, and their traffic is increasing explosively every year, thus increasing the pressure of video transmission. Video coding technology has become the key to compressing original videos. As an indispensable technology, rate control [...] Read more.
Video data have become the main data traffic on the Internet, and their traffic is increasing explosively every year, thus increasing the pressure of video transmission. Video coding technology has become the key to compressing original videos. As an indispensable technology, rate control plays an important role in stabilizing video stream transmission. Rate control (RC) is part of rate distortion optimization (RDO) whose job is to find the optimal solution based on balancing rate and distortion. It not only needs to consider the buffer and network status but also adjust the corresponding bit rate according to the video content. This paper reviews the related technologies of rate control under high efficiency video coding (HEVC) and versatile video coding (VVC) standards so that subsequent researchers can quickly understand the field and promote the development of rate control algorithms. Firstly, the paper summarizes the various aspects of RC, including basic principles, rate-distortion models, major processes, and performance criteria. Secondly, the paper surveys, in detail, the research progress in the field of rate control and analyzes several mainstream research directions. Thirdly, we carry out relevant experiments on the standard reference software and analyze and discuss the experimental results of the existing studies. Finally, we look ahead to the future trends of rate control and provide feasible improvement suggestions. Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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14 pages, 804 KiB  
Article
Multitask Learning Based Intra-Mode Decision Framework for Versatile Video Coding
by Naima Zouidi, Amina Kessentini, Wassim Hamidouche, Nouri Masmoudi and Daniel Menard
Electronics 2022, 11(23), 4001; https://doi.org/10.3390/electronics11234001 - 02 Dec 2022
Cited by 3 | Viewed by 1288
Abstract
In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, [...] Read more.
In mid-2020, the new international video coding standard, namely versatile video coding (VVC), was officially released by the Joint Video Expert Team (JVET). As its name indicates, the VVC enables a higher level of versatility with better compression performance compared to its predecessor, high-efficiency video coding (HEVC). VVC introduces several new coding tools like multiple reference lines (MRL) and matrix-weighted intra-prediction (MIP), along with several improvements on the block-based hybrid video coding scheme such as quatree with nested multi-type tree (QTMT) and finer-granularity intra-prediction modes (IPMs). Because finding the best encoding decisions is usually preceded by optimizing the rate distortion (RD) cost, introducing new coding tools or enhancing existing ones requires additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, this paper aims to reduce the computational complexity of the VVC. It establishes a large database for intra-prediction and proposes a multitask learning (MTL)-based intra-mode decision framework. Experimental results show that our proposal enables up to 30% of complexity reduction while slightly increasing the Bjontegaard bit rate (BD-BR). Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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21 pages, 2275 KiB  
Article
Fast Decision Algorithm of CU Size for HEVC Intra-Prediction Based on a Kernel Fuzzy SVM Classifier
by Shuqian He, Zhengjie Deng and Chun Shi
Electronics 2022, 11(17), 2791; https://doi.org/10.3390/electronics11172791 - 05 Sep 2022
Cited by 3 | Viewed by 1203
Abstract
High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve [...] Read more.
High Efficiency Video Coding (HEVC) achieves a significant improvement in compression efficiency at the cost of extremely high computational complexity. Therefore, large-scale and wide deployment applications, especially mobile real-time video applications under low-latency and power-constrained conditions, are more challenging. In order to solve the above problems, a fast decision method for intra-coding unit size based on a new fuzzy support vector machine classifier is proposed in this paper. The relationship between the depth levels of coding units is accurately expressed by defining the cost evaluation criteria of texture and non-texture rate-distortion cost. The fuzzy support vector machine is improved by using the information entropy measure to solve the negative impact of data noise and the outliers problem. The proposed method includes three stages: the optimal coded depth level “0” early decision, coding unit depth early skip, and optimal coding unit early terminate. In order to further improve the rate-distortion complexity optimization performance, more feature vectors are introduced, including features such as space complexity, the relationship between coding unit depths, and rate-distortion cost. The experimental results showed that, compared with the HEVC reference test model HM16.5, the proposed algorithm can reduce the encoding time of various test video sequences by more than 53.24% on average, while the Bjontegaard Delta Bit Rate (BDBR) only increases by 0.82%. In addition, the proposed algorithm is better than the existing algorithms in terms of comprehensively reducing the computational complexity and maintaining the rate-distortion performance. Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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23 pages, 6917 KiB  
Article
Image Segmentation Methods for Subpicture Partitioning in the VVC Video Encoder
by Natalia Panagou, Panagiotis Belememis and Maria Koziri
Electronics 2022, 11(13), 2070; https://doi.org/10.3390/electronics11132070 - 01 Jul 2022
Viewed by 1774
Abstract
The emergence of the new generation video coding standard, Versatile Video Coding (VVC), has brought along novel features rendering the new standard more efficient and flexible than its predecessors. Aside from efficient compression of 8 k or higher camera-captured content, VVC also supports [...] Read more.
The emergence of the new generation video coding standard, Versatile Video Coding (VVC), has brought along novel features rendering the new standard more efficient and flexible than its predecessors. Aside from efficient compression of 8 k or higher camera-captured content, VVC also supports a wide range of applications, including computer-generated content, high dynamic range (HDR) content, multilayer and multi-view coding, video region extraction, as well as 360° video. One of the newly introduced coding tools in VVC, offering extraction and independent coding of rectangular sub-areas within a frame, is called Subpicture. In this work, we turn our attention to frame partitioning using Subpictures in VVC, and more particularly, a content-aware partitioning is considered. To achieve that, we make use of image segmentation algorithms and properly modify them to operate on a per Coding Tree Unit (CTU) basis in order to render them compliant with the standard’s restrictions. Additionally, since subpicture boundaries need to comply with slice boundaries, we propose two methods for properly partitioning a frame using tiles/slices aiming to avoid over-partitioning of a frame. The proposed algorithms are evaluated regarding both compression efficiency and image segmentation effectiveness. Our evaluation results indicate that the proposed partitioning schemes have a negligible impact on compression efficiency and video quality Full article
(This article belongs to the Special Issue Video Coding, Processing, and Delivery for Future Applications)
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