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Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes: 2nd Edition
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Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1018

Special Issue Editors

Dr. Jinjia Zhou

E-Mail Website
Guest Editor
Intelligent Media Processing Lab, Hosei University, Tokyo 102-8160, Japan
Interests: image sensors; computer vision; image processing; video coding
Special Issues, Collections and Topics in MDPI journals
Dr. Ittetsu Taniguchi

E-Mail Website
Guest Editor
Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
Interests: image/video processing for embedded system; design methodology for embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xin Jin

E-Mail Website
Guest Editor
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
Interests: computational photography; image/video processing and coding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is an increasing interest in the development of intelligent sensor nodes that enable intelligent processing for Internet of Things (IoT) surveillance, remote sensing, and smart city applications. The data are processed onboard through embedded signal processing and machine learning-based analysis algorithms. These machine learning-driven sensors can transmit key information instead of raw sensing data, thereby lowering the data volume traveling through a network.

In recent years, there has been a preference for specifically designed image and video codecs because of the explosion of image and video data in IoT systems. Indeed, this is due to a focus on reducing data burden and improving reconstructed image quality, image/video coding and processing techniques for low-cost implementations, reducing power consumption, and increasing battery lifetimes that can cope with the design requirements of sensor nodes. Moreover, intelligent sensors can make the jump from traditional intuition-driven sensors to machine learning algorithms, thus delivering high-resolution images and videos for the 5G revolution.

In line with the mission of Sensors, the organizers of this Special Issue endeavor to demonstrate the most recent advancements in image/video coding and processing techniques for intelligent sensor nodes from both academic and industrial perspectives.

Dr. Jinjia Zhou
Dr. Ittetsu Taniguchi
Prof. Dr. Xin Jin
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. Sensors 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 2600 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 coding
  • image sensing
  • image/video processing
  • wireless communication
  • wireless sensor network
  • computational imaging

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Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 798 KiB  
Article
A Lightweight Double Compression Detector for HEIF Images Based on Encoding Information
by Yoshihisa Furushita, Marco Fontani, Stefano Bianchi, Alessandro Piva and Giovanni Ramponi
Sensors 2024, 24(16), 5103; https://doi.org/10.3390/s24165103 - 6 Aug 2024
Viewed by 279
Abstract
Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it [...] Read more.
Extensive research has been conducted in image forensics on the analysis of double-compressed images, particularly in the widely adopted JPEG format. However, there is a lack of methods to detect double compression in the HEIF format, which has recently gained popularity since it allows for reduced file size while maintaining image quality. Traditional JPEG-based techniques do not apply to HEIF due to its distinct encoding algorithms. We previously proposed a method to detect double compression in HEIF images based on Farid’s work on coding ghosts in JPEG images. However, this method was limited to scenarios where the quality parameter used for the first encoding was larger than for the second encoding. In this study, we propose a lightweight image classifier to extend the existing model, enabling the identification of double-compressed images without heavily depending on the input image’s quantization history. This extended model outperforms the previous approach and, despite its lightness, demonstrates excellent detection accuracy. Full article
(This article belongs to the Special Issue Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes: 2nd Edition)
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22 pages, 18896 KiB  
Article
Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing
by Luyang Liu, Hiroki Nishikawa, Jinjia Zhou, Ittetsu Taniguchi and Takao Onoye
Sensors 2024, 24(13), 4348; https://doi.org/10.3390/s24134348 - 4 Jul 2024
Viewed by 534
Abstract
Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost [...] Read more.
Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. Full article
(This article belongs to the Special Issue Image/Video Coding and Processing Techniques for Intelligent Sensor Nodes: 2nd Edition)
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