Journal Information
Signal Processing: Image Communication (SPIC)
Impact Factor:

Call For Papers
Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following:

To present a forum for the advancement of theory and practice of image communication.

To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems.

To contribute to a rapid information exchange between the industrial and academic environments.

The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world.

Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments.

Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
Last updated by Dou Sun in 2019-12-08
Special Issues
Special Issue on Visual Information Processing for Underwater Images and Videos: Theories, Algorithms, and Applications
Submission Date: 2020-05-01

Underwater images and videos play significant roles in developing, exploring, and protecting the underwater world. However, there are many challenges that need to be addressed due to the complex and uncontrollable underwater imaging conditions. Underwater images and videos taken by sensors always suffer from the effects of quality degradation due to light selective absorption and scattering as well as the use of artificial light. The degraded underwater data have low contrast and brightness, color deviations, blurry details, nonuniform bright speck, etc., which not only affects the experience of human perception but also challenges the computer vision algorithms. Despite the prolific work in underwater visual information processing, the related theories are antique and current algorithms remain largely unsatisfactory. Further, it is difficult to achieve decent performance by directly transplanting the computer vision applications (e.g., object detection, recognition, segmentation, etc.) for conventional images and videos into underwater ones. Besides, large-scale real-world underwater benchmark datasets and specialized underwater image and video quality assessment metrics are lacking, which keep this research area at a standstill. Thus, there is a pressing demand for novel theories and algorithms that can effectively deal with the problems of underwater image and video quality degradation, accurately evaluate underwater image and video quality and efficiently compress underwater images and videos, and also call for the various applications of computer vision algorithms in underwater images and videos. This special issue will feature original research papers related to the theories and algorithms for underwater visual information processing together with widespread applications. The main topics of interest are, but are not limited to: Underwater optical imaging physical model and quality degradation theory.  Underwater image and video enhancement and restoration algorithms, including traditional methods, physical model-based methods, and deep learning-based methods.  Underwater image and video quality assessment methods, including full-reference assessment metrics, non-reference assessment metrics, etc.  Underwater image and video compression, coding, representation, transformation, etc.  Underwater image and video applications: aquatic robotics visions, underwater machine visions, object detection, object recognition, segmentation, tracking, 3D modeling, etc.  New benchmark datasets related to the aforementioned topics.
Last updated by Dou Sun in 2019-12-08
Special Issue on Explainable AI on Emerging Multimedia Technologies
Submission Date: 2020-06-01

Recently, the multimedia landscape underwent a revolution around several technological innovations. Although these new innovations are not massively adopted on the market yet, they show a promising perspective on the future of multimedia consumption. These emerging multimedia technologies lead to a plethora of new questions about compression, transmission, perception, and finally QoE. The advent of machine learning, especially deep learning and AI, coeval with large scale of media, has impacted on various aspects of our research and applications. The advantageous but typically unstructured large amount of multimedia content comes from an assortment of sources, in various modalities, and offer diverse levels of knowledge. It gives rise to a great multimedia challenge that concerns not only the fusion of multi-source multimedia data, but also the needs to offer insights, tackle real-word problems and solutions, and serve the intended users and communities These types of challenges have resulted in a far-reaching surge of interest in AI. This is mainly due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. In all such applications, it is of paramount importance to understand, trust, and in one word “explain” the rationale behind AI models' decisions. Although different efforts have been initiated recently to explain behaviour and decisions of these models through Explainable AI, which aims at reasoning about the behaviour and decisions, is still in its inception in the field of multimedia. This special issue will provide new insights, tools and technologies specific for Explainable AI on emerging multimedia technologies Topics of interest: Explainable AI in multimedia compression, transmission, and perception Explainable AI in multimedia content retrieval, personalization and recommendation Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models Responsible artificial intelligence: designing AI for human values Architectures, algorithms and tools to support explainability Reconfigurable processor for deep learning in multimedia technologies Explainable AI applications and practices Explainable AI for Quality of Experience (QoE) of emerging media, including omnidirectional video (360° video), light field imaging, and High Dynamic Range (HDR) video. Explainable AI for user behavior and viewing behavior when interacting emerging media technologies. Explainable AI Perceptual analysis and models for emerging media technologies. Explainable AI Objective metrics for emerging media innovations.
Last updated by Dou Sun in 2019-12-08
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