Información de la Revista
Signal Processing: Image Communication (SPIC)
Factor de Impacto:

Solicitud de Artículos
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.
Última Actualización Por Dou Sun en 2019-12-08
Special Issues
Special Issue on Computer Vision for Augmented Reality
Día de Entrega: 2020-09-15

Augmented reality (AR) is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter AR technology, which can be used to visualize data from hundreds of sensors (Kinect, HoloLens, Intel Real Sense, and so on) simultaneously, overlaying relevant and actionable information over your environment through a headset. However, most augmented reality experiences today revolve around overlaying the physical world with known information. Maps and games have garnered much attention in the consumer tech space. In the practical applications, the AR capabilities being leveraged would be constituted as visualize, instruct, or guide. Some examples: Virtual work instructions for operating manuals, Service maintenance timely imprint digitized information in the real-world and in-context to the task at hand. Artificial intelligence – and especially deep learning – ushers in a new wave of innovation to computer vision (CV) and augmented reality (AR). The ability to perceive an array of environments will unlock the next-generation of augmented reality use cases and further empower the front-line worker like never before. Understanding the differences between classical (or traditional) and learning computer vision is fundamental to developing applications today and in the near future. Industrial environments are extremely complex. Augmented reality technology based on vision is not only an effective data visualization technology, but also can train workers for operating machines effectively. This essentially is the ‘design-your-own’ CV algorithm in a design and coding environment. An engineer can map native sensor inputs to 3D geometries and enable the CV algorithm to be recognized for a specific use case. The AR engineer or experience creator can bring this CV algorithm and specific use case to life in a 3D design authoring environment by aligning these geometries, points, features, and measurements to activate it in context. Computer vision and more specifically deep learning-based approaches embedded in the augmented reality application enabled this automatic object recognition. In summary, the convergence of computer vision and augmented reality is a really cool upcoming wave. As a result, this special session aims to bring the latest results over computer vision for augmented reality. It can help technicians to exchange the latest technical progresses. Topics include, but are not limited to: Camera tracking for augmented reality Deep learning for computer vision 3D object reconstruction in augmented reality 3D object recognition 3D object tracking in augmented reality Color consistency in augmented reality Color transfer in augmented reality Communication between augmented reality devices Real-world Applications of augmented reality: security; healthcare; and advertising
Última Actualización Por Dou Sun en 2020-06-07
Special Issue on Multimedia Big Data Privacy and Processing Based on Compressive Sensing
Día de Entrega: 2020-09-30

Multimedia data that include image and video are the biggest ‘big’ data and their applications, such as social media, healthcare, and video surveillance, are ubiquitous. However, multimedia big data (MMBD) face several privacy issues: the privacy issues of MMBD themselves through information leakage and data tampering, and the privacy issues during the processing of MMBD, such as data acquisition, data storage, and data analysis. Compressive sensing (CS) as an emerging light-weight encryption mechanism is receives widespread attention. CS can bring a confidentiality guarantee when random measurement matrix acts as a key and would provide privacy protection for MMBD. For example, CS-based encryption is for data confidentiality and CS-based watermarking is for data integrity. Moreover, CS requires fewer samples to achieve the same reconstruction performance for the signal compared with the traditional Shannon-Nyquist sampling. CS also has the strong robustness of recovering the signal even if samples are contaminated by noise. Thus, CS has very good characteristics for privacy-preserving MMBD processing applications. Examples of potential privacy-preserving processing include low-complexity sampling for data acquisition; compression during sampling for data storage; and reconstruction robustness for data analysis. This Special Issue aims at collecting different architectures, solutions, mechanisms to understand and exploit the potential of CS for multimedia big data privacy and processing. Topics of interest include, but are not limited to: MMBD encryption based on CS MMBD steganography based on CS MMBD watermarking, information hiding, and hashing based on CS MMBD authentication and forensics based on CS Privacy-preserving MMBD aggregation Privacy-preserving MMBD representation and coding based on CS Privacy-preserving MMBD recognition based on CS measurements Privacy-preserving MMBD classification, restoration, and optimization based on CS and sparsity
Última Actualización Por Dou Sun en 2020-06-07
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Nombre CompletoFactor de ImpactoEditor
IEEE Signal Processing Magazine6.671IEEE
IEEE Wireless Communications9.202IEEE
IEEE Transactions on Wireless Communications6.394IEEE
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IEEE Transactions on Signal Processing5.230IEEE
Journal of Universal Computer Science0.546Verlag der Technischen Universitat Graz
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