SIGNAL PROCESSING-IMAGE COMMUNICATION
Scope & Guideline
Exploring the Synergy of Vision and Communication
Introduction
Aims and Scopes
- Image Processing Techniques:
The journal emphasizes the development and application of advanced image processing techniques, including image enhancement, restoration, and segmentation methodologies. - 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. - 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. - 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. - 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. - 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.
Trending and Emerging
- 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. - 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. - 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. - 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. - 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
- 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. - 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. - 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. - 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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