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Electronics
Special Issues
Challenges and Applications in Multimedia and Visual Computing
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Challenges and Applications in Multimedia and Visual Computing

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 3041

Special Issue Editors

Prof. Dr. Jianjun Lei

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: multimedia communication; 3D video processing; virtual reality; artificial intelligence; pattern recognition
Dr. Bo Peng

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: image/video processing; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shuai Li

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan 250061, China
Interests: 3D vision; image/video coding and processing; IndRNN
Special Issues, Collections and Topics in MDPI journals
Dr. Bin Yi

E-Mail Website
Guest Editor
Institute of Data Science (IDS), National University of Singapore, Singapore 117602, Singapore
Interests: multimedia analysis; multimodal learning; deep learning

Special Issue Information

Dear Colleagues,

Multimedia and visual computing are important research areas in the fields of machine learning and computer vision. Over the past decade, various types of visual content have shown rapid expansion, including texts, images, videos, point clouds, and so on. It is essential to represent and understand such a large amount of visual content for multimedia and visual computing tasks. Meanwhile, the rise of artificial intelligence (AI) technology is able to provide effective support for deriving valuable information from various types of visual content. However, due to the complex characteristics of real-world visual content, there are still some difficulties in handling multimedia and visual computing tasks with existing AI-driven algorithms. Therefore, it is significant to explore advanced multimedia and visual computing methods for practical applications, thus promoting the development of an intelligent society.

The purpose of this Special Issue is to collect and publish new ideas, the latest findings, and state-of-the-art achievements on the topic of challenges and applications in multimedia and visual computing.

Topics of interest include, but are not limited to:

  1. Advanced visual feature learning and representation methods;
  2. Advanced classification, recognition, and retrieval methods;
  3. Advanced 2D/3D object detection and tracking methods;
  4. Advanced multi-modal learning and analysis methods;
  5. Advanced stereo, multi-view, and 3D content processing methods;
  6. Advanced image/video synthesis, editing, and visualization methods;
  7. Advanced image/video restoration and enhancement methods in low-level vision;
  8. Advanced quality assessment methods for multimedia;
  9. Advanced image/video compression and intelligent analysis methods;
  10. Zero-shot/few-shot methods for visual computing and multimedia;
  11. Unsupervised/self-supervised/weakly supervised methods for visual computing and multimedia.

Prof. Dr. Jianjun Lei
Dr. Bo Peng
Prof. Dr. Shuai Li
Dr. Bin Yi
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

  • image/video processing
  • image/video compression
  • quality assessment
  • restoration and enhancement
  • representation learning
  • recognition and retrieval
  • 3D processing
  • multi-modal learning
  • multimedia analysis

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Published Papers (2 papers)

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Research

15 pages, 1240 KiB  
Article
Position-Guided Multi-Head Alignment and Fusion for Video Super-Resolution
by Yanbo Gao, Xun Cai, Shuai Li, Jiajing Chai and Chuankun Li
Electronics 2024, 13(22), 4372; https://doi.org/10.3390/electronics13224372 - 7 Nov 2024
Viewed by 801
Abstract
Video super-resolution (VSR), which takes advantage of multiple low-resolution (LR) video frames to reconstruct corresponding high-resolution (HR) frames in a video, has raised increasing interest. To upsample an LR frame (denoted by a reference frame), VSR methods usually align multiple neighboring frames (denoted [...] Read more.
Video super-resolution (VSR), which takes advantage of multiple low-resolution (LR) video frames to reconstruct corresponding high-resolution (HR) frames in a video, has raised increasing interest. To upsample an LR frame (denoted by a reference frame), VSR methods usually align multiple neighboring frames (denoted by supporting frames) to the reference frame first in order to provide more relevant information. The existing VSR methods usually employ deformable convolution to conduct the frame alignment, where the whole supporting frame is aligned to the reference frame without a specific target and without supervision. Thus, the aligned features are not explicitly learned to provide the HR frame information and cannot fully explore the supporting frames. To address this problem, in this work, we propose a novel video super-resolution framework with Position-Guided Multi-Head Alignment, termed as PGMH-A, to explicitly align the supporting frames to different spatial positions of the HR frame (denoted by different heads). It injects explicit position information to obtain multi-head-aligned features of supporting frames to better formulate the HR frame. PGMH-A can be trained individually or end-to-end with the ground-truth HR frames. Moreover, a Position-Guided Multi-Head Fusion, termed as PGMH-F, is developed based on the attention mechanism to further fuse the spatial–temporal information across temporal supporting frames, across multiple heads corresponding to the different spatial positions of an HR frame, and across multiple channels. Together, the proposed Position-Guided Multi-Head Alignment and Fusion (PGMH-AF) can provide VSR with better local details and temporal coherence. The experimental results demonstrate that the proposed method outperforms the state-of-the-art VSR networks. Ablation studies have also been conducted to verify the effectiveness of the proposed modules. Full article
(This article belongs to the Special Issue Challenges and Applications in Multimedia and Visual Computing)
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14 pages, 9314 KiB  
Article
Real–Virtual 3D Scene-Fused Integral Imaging Based on Improved SuperPoint
by Wei Wu, Shigang Wang, Wanzhong Chen, Hao Wang and Cheng Zhong
Electronics 2024, 13(5), 970; https://doi.org/10.3390/electronics13050970 - 3 Mar 2024
Cited by 1 | Viewed by 1508
Abstract
To enrich 3D scenes, a real–virtual fusion-based integral imaging method is proposed. It combines the Softargmax function with Gaussian weighting coefficients for sub-pixel feature point extraction from SuperPoint detection results. SIFT is also used for feature point detection and matching, along with the [...] Read more.
To enrich 3D scenes, a real–virtual fusion-based integral imaging method is proposed. It combines the Softargmax function with Gaussian weighting coefficients for sub-pixel feature point extraction from SuperPoint detection results. SIFT is also used for feature point detection and matching, along with the improved SuperPoint. Subsequently, based on the multi-view 3D reconstruction, the real object is reconstructed into a 3D model. A virtual model is then fused with the 3D reconstructed model of the real object to generate a real–virtual fusion elemental image array based on the display platform’s optical parameters. The experimental results demonstrate that the proposed method can optically reconstruct more realistic and vivid real–virtual fusion 3D images. This method can enrich a scene’s content, enhance visualization and interactivity, save costs and time, and provide flexibility and customization. Full article
(This article belongs to the Special Issue Challenges and Applications in Multimedia and Visual Computing)
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