SIGNAL PROCESSING-IMAGE COMMUNICATION

Scope & Guideline

Elevating Standards in Image Communication Research

Introduction

Delve into the academic richness of SIGNAL PROCESSING-IMAGE COMMUNICATION with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0923-5965
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1989 to 2024
AbbreviationSIGNAL PROCESS-IMAGE / Signal Process.-Image Commun.
Frequency10 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal SIGNAL PROCESSING-IMAGE COMMUNICATION is dedicated to advancing the field of image processing and communication, focusing on the development of innovative techniques, algorithms, and applications. It encompasses a wide range of topics that are integral to the enhancement, analysis, and interpretation of visual information.
  1. Image Processing Techniques:
    The journal emphasizes the development and application of advanced image processing techniques, including image enhancement, restoration, and segmentation methodologies.
  2. Computer Vision and Machine Learning:
    There is a strong focus on the integration of machine learning and deep learning methods in computer vision tasks, facilitating improved object detection, recognition, and scene understanding.
  3. Multimedia Communication:
    Research related to multimedia communication, including video coding, streaming, and quality assessment, is a core area, addressing the challenges in efficient transmission and representation of visual data.
  4. Quality Assessment and Enhancement:
    The journal explores methods for assessing and improving image and video quality, focusing on no-reference quality metrics and enhancement algorithms.
  5. Cross-Modal and Multimodal Processing:
    Research on cross-modal processing, particularly in the context of integrating data from different modalities (e.g., RGB-D images, infrared-visible fusion) is highlighted.
  6. Application-Specific Innovations:
    The journal also encourages submissions that demonstrate the application of image processing techniques in specific domains such as medical imaging, autonomous driving, underwater imaging, and more.
Recent publications indicate several emerging themes and trends within the journal, reflecting the dynamic nature of the field and the integration of new technologies.
  1. Deep Learning in Image Processing:
    The journal has seen a surge in papers applying deep learning techniques to various image processing tasks, including super-resolution, segmentation, and enhancement, indicating a trend towards AI-driven methodologies.
  2. Underwater and Low-Light Imaging:
    Research focusing on underwater image enhancement and low-light imaging techniques is gaining traction, highlighting the need for specialized solutions in challenging imaging environments.
  3. Quality of Experience (QoE) in Multimedia:
    There is an increasing emphasis on the assessment and enhancement of user experience in multimedia applications, particularly concerning video quality and user satisfaction metrics.
  4. Real-time Processing and Applications:
    Emerging themes include real-time image processing and applications, particularly in areas such as autonomous systems and interactive media, reflecting the demand for efficiency and speed.
  5. Cross-Modal Learning:
    Growing interest in cross-modal learning, where techniques leverage information from multiple modalities (e.g., RGB and depth) for improved performance in tasks like object detection and recognition.

Declining or Waning

While the journal continues to publish a broad spectrum of research, some themes appear to be declining in prominence. This may reflect shifts in research focus or the maturation of certain methodologies.
  1. Traditional Image Coding Techniques:
    There has been a noticeable reduction in papers focused on traditional image coding methods as newer, more efficient techniques emerge, particularly those leveraging machine learning.
  2. Basic Image Enhancement Techniques:
    Basic image enhancement methods seem to be less prevalent, possibly due to the rise of more sophisticated approaches that utilize deep learning for enhancement tasks.
  3. Manual Feature Extraction Methods:
    Research centered around manual or heuristic feature extraction techniques is waning, as deep learning methods that automatically learn features become the standard.
  4. Theoretical Analysis without Practical Application:
    Papers focusing solely on theoretical aspects of image processing without practical implementations or applications are becoming less common, reflecting a shift towards applied research.

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