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 Multimedia Big Data Privacy and Processing Based on Compressive Sensing
Submission Date: 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
Last updated by Dou Sun in 2020-06-07
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