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Applied Sciences
Special Issues
Novel Research on Image and Video Processing Technology
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Novel Research on Image and Video Processing Technology

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

Deadline for manuscript submissions: 20 October 2024 | Viewed by 2280

Special Issue Editor

Dr. Samuel Cheng

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019-1102, USA
Interests: information theory; signal and image processing

Special Issue Information

Dear Colleagues,

MDPI Applied Sciences is pleased to announce a Call for Papers for an upcoming Special Issue on "Novel Research on Image and Video Processing Technology". We invite authors from academia, industry, and research institutions globally to contribute their high-quality original research and review articles for this Issue.

The rapidly evolving field of Image and Video Processing Technology has established a new paradigm in various scientific and technological fields, including computer science, engineering, telecommunications, robotics, and artificial intelligence. This Special Issue seeks to publish research articles and reviews that provide significant advances and breakthroughs in the following topics, including but not limited to:

  • Advanced algorithms for image and video processing;
  • Machine learning and AI in image and video processing;
  • Deep learning techniques in video and image recognition;
  • Augmented Reality and Virtual Reality image processing;
  • Computational photography and videography;
  • 3D image and video processing and analysis;
  • Image and video compression, coding, and encryption;
  • Real-time image and video processing;
  • Biometric image processing;
  • Medical image and video analysis.

Dr. Samuel Cheng
Guest Editor

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

  • image and video processing algorithms
  • AI/ML in image and video processing
  • AR/VR image and video processing
  • 3D image and video processing
  • medical image and video analysis

Published Papers (3 papers)

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Research

16 pages, 524 KiB  
Article
Non-Iterative Cluster Routing: Analysis and Implementation Strategies
by Huong Pham and Samuel Cheng
Appl. Sci. 2024, 14(5), 1706; https://doi.org/10.3390/app14051706 - 20 Feb 2024
Viewed by 369
Abstract
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust [...] Read more.
In conventional routing, a capsule network employs routing algorithms for bidirectional information flow between layers through iterative processes. In contrast, the cluster routingtechnique utilizes a non-iterative process and can outperform state-of-the-art models with fewer parameters, while preserving the part–whole relationship and demonstrating robust generalization to novel viewpoints. This paper aims to further analyze and clarify this concept, providing insights that allow users to implement the cluster routing technique efficiently. Additionally, we expand the technique and propose variations based on the routing principle of achieving consensus among votes in distinct clusters. In some cases, these variations have the potential to enhance and boost the cluster routing performance while utilizing similar memory and computing resources. Full article
(This article belongs to the Special Issue Novel Research on Image and Video Processing Technology)
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13 pages, 860 KiB  
Article
Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding
by Guojie Chen and Min Lin
Appl. Sci. 2024, 14(4), 1653; https://doi.org/10.3390/app14041653 - 18 Feb 2024
Viewed by 644
Abstract
Lossless coding is a compression method in the Versatile Video Coding (VVC) standard, which can compress video without distortion. Lossless coding has great application prospects in fields with high requirements for video quality. Since the current VVC standard is mainly designed for lossy [...] Read more.
Lossless coding is a compression method in the Versatile Video Coding (VVC) standard, which can compress video without distortion. Lossless coding has great application prospects in fields with high requirements for video quality. Since the current VVC standard is mainly designed for lossy coding, the compression efficiency of VVC lossless coding makes it hard to meet people’s needs. In order to improve the performance of VVC lossless coding, this paper proposes a sample-based intra-gradient edge detection and angular prediction (SGAP) method. SGAP utilizes the characteristics of lossless intra-coding to employ samples adjacent to the current sample as reference samples and performs prediction through sample iteration. SGAP aims to improve the prediction accuracy for edge regions, smooth regions and directional texture regions in images. Experimental results on the VVC Test Model (VTM) 12.3 reveal that SGAP achieves 7.31% bit-rate savings on average in VVC lossless intra-coding, while the encoding time is only increased by 5.4%. Compared with existing advanced sample-based intra-prediction methods, SGAP can provide significantly higher coding performance gain. Full article
(This article belongs to the Special Issue Novel Research on Image and Video Processing Technology)
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15 pages, 8918 KiB  
Article
A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
by Haiwu Zhao, Shuai Zhao, Xiwu Shang and Guozhong Wang
Appl. Sci. 2023, 13(18), 10381; https://doi.org/10.3390/app131810381 - 17 Sep 2023
Viewed by 836
Abstract
Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list [...] Read more.
Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%. Full article
(This article belongs to the Special Issue Novel Research on Image and Video Processing Technology)
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