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Electronics
Special Issues
Image and Video Coding Technology
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Image and Video Coding Technology

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 March 2025) | Viewed by 2463

Special Issue Editors

Dr. Bin Chen

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: information theory; data compression; algebraic coding theory; machine learning; deep learning; distributed storage
Special Issues, Collections and Topics in MDPI journals
Dr. Shuhan Qi

E-Mail Website
Guest Editor
Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Interests: multimedia information retrieval
Dr. Yaowei Wang

E-Mail Website
Guest Editor
Peng Cheng Laboratory, University of Science and Technology of China, Shenzhen 518000, China
Interests: intelligent transportation

Special Issue Information

Dear Colleagues,

In recent years, deep image/video coding technologies represented by deep learning have significantly outperformed traditional image/video coding formats such as JPEG and HEVC in terms of human perception and machine vision tasks. The coding quality of images/videos depends on various quality evaluation metrics, and novel perceptual metrics have led to higher requirements for image and video compression. At the same time, the development of large-scale foundation models has provided good data priors for image and video compression, creating possibilities for further enhancing the machine vision accuracy of image/video coding. However, for practical application deployment, designing low-latency and low-complexity image/video coding algorithms is still a key problem to be solved.

In this context, for this Special Issue entitled “Image and Video Coding Technology”, we invite original research and comprehensive reviews on, but not limited to, the following:

  • Advances in perceptual image/video coding;
  • Advances in lossy and lossless image/video coding;
  • Advances in point cloud coding;
  • Advances in machine vision-oriented image/video coding;
  • Advances in image/video compressive sensing;
  • Reinforcement learning and game theory for image/video coding;
  • Real-time video encoding;
  • Scalable Video Coding (SVC);
  • Enabling technologies for high-performance video coding;
  • Video coding for mobile communication;
  • Rate distortion theory for lossy image/video coding.

Dr. Bin Chen
Dr. Shuhan Qi
Dr. Yaowei Wang
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

  • advances in perceptual image/video coding
  • advances in lossy and lossless image/video coding
  • advances in point cloud coding
  • advances in machine vision-oriented image/video coding
  • advances in image/video compressive sensing
  • reinforcement learning and game theory for image/video coding
  • real-time video encoding
  • scalable video coding (SVC)
  • enabling technologies for high-performance video coding
  • video coding for mobile communication
  • rate distortion theory for lossy image/video coding

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Published Papers (2 papers)

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Research

23 pages, 4436 KiB  
Article
JSN: Design and Analysis of JPEG Steganography Network
by Po-Chyi Su, Yi-Han Cheng and Tien-Ying Kuo
Electronics 2024, 13(23), 4821; https://doi.org/10.3390/electronics13234821 - 6 Dec 2024
Cited by 1 | Viewed by 695
Abstract
Image steganography involves hiding a secret message within an image for covert communication, allowing only the intended recipient to extract the hidden message from the “stego” image. The secret message can also be an image itself to enable the transmission of more information, [...] Read more.
Image steganography involves hiding a secret message within an image for covert communication, allowing only the intended recipient to extract the hidden message from the “stego” image. The secret message can also be an image itself to enable the transmission of more information, resulting in applications where one image is concealed within another. While existing techniques can embed a secret image of similar size into a cover image with minimal distortion, they often overlook the effects of lossy compression during transmission, such as when saving images in the commonly used JPEG format. This oversight can hinder the extraction of the hidden image. To address the challenges posed by JPEG compression in image steganography, we propose a JPEG Steganography Network (JSN) that leverages a reversible deep neural network as its backbone, integrated with the JPEG encoding process. We utilize 8×8 Discrete Cosine Transform (DCT) and consider the quantization step size specified by JPEG to create a JPEG-compliant stego image. We also discuss various design considerations and conduct extensive testing on JSN to validate its performance and practicality in real-world applications. Full article
(This article belongs to the Special Issue Image and Video Coding Technology)
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16 pages, 1324 KiB  
Article
Fast Hybrid Search for Automatic Model Compression
by Guilin Li , Lang Tang and Xiawu Zheng
Electronics 2024, 13(4), 688; https://doi.org/10.3390/electronics13040688 - 7 Feb 2024
Viewed by 1212
Abstract
Neural network pruning has been widely studied for model compression and acceleration, to facilitate model deployment in resource-limited scenarios. Conventional methods either require domain knowledge to manually design the pruned model architecture and pruning algorithm, or AutoML-based methods to search the pruned model [...] Read more.
Neural network pruning has been widely studied for model compression and acceleration, to facilitate model deployment in resource-limited scenarios. Conventional methods either require domain knowledge to manually design the pruned model architecture and pruning algorithm, or AutoML-based methods to search the pruned model architecture but still prune all layers with a single pruning algorithm. However, many pruning algorithms have been proposed and they all differ regarding the importance they attribute to the criterion of filters. Therefore, we propose a hybrid search method, searching for the pruned model architecture and the pruning algorithm at the same time, which automatically finds the pruning ratio and pruning algorithm for each convolution layer. Moreover, to be more efficient, we divide the search process into two phases. Firstly, we search in a huge space with adaptive batch normalization, which is a fast but relatively inaccurate model evaluation method; secondly, we search based on the previous results and evaluate models by fine-tuning, which is more accurate. Therefore, our proposed hybrid search method is efficient, and achieves a clear improvement in performance compared to current state-of-the-art methods, including AMC, MetaPruning, and ABCPruner. For example, when pruning MobileNet, we achieve a 59.8% test accuracy on ImageNet with only 49 M FLOPs, which is 2.6% higher than MetaPruning. Full article
(This article belongs to the Special Issue Image and Video Coding Technology)
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