Journal Information
Signal Processing: Image Communication
http://www.journals.elsevier.com/signal-processing-image-communication/
Impact Factor:
2.244
Publisher:
ELSEVIER
ISSN:
0923-5965
Viewed:
3615
Tracked:
4

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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 2017-08-05
Special Issues
Special Issue on Tensor Image Processing
Submission Date: 2018-02-09

Tensor (i.e. multidimensional array) is a natural representation for image and video. The related advances in applied mathematics allow us to gradually move from classical matrix based methods to tensor methods for image processing methods and applications. The resulted new research topic, called tensor image processing, offers new tools to exploit the multi-dimensional and intrinsic structures in image data. In this inter-disciplinary research field, there are fast emerging works on tensor theory, tensor based models, numerical computation and efficient algorithms, and applications on image and video processing. This special issue aims to collect the latest original contributions in tensor image processing, and offer new ideas, experiences and discussions by experts in this field. We encourage the submission of papers with new theory, analysis, methods, and applications in tensor image processing. The list of possible topics of interest include, but are not limited to: - tensor factorization/decomposition and its analysis - tensor computation - low rank tensor approximation - tensor regression and classification - tensor independent component analysis - tensor principal component analysis - tensor dictionary learning - tensor subspace clustering - tensor based blind source separation - tensor image data fusion - tensor image compression - tensor image completion - tensor image denoising/deblurring - tensor image segmentation - tensor image registration - tensor image feature extraction - tensor Image Interpolation - tensor image’s quality assessment
Last updated by Dou Sun in 2017-08-05
Special Issue on Advances in statistical methods-based visual quality assessment
Submission Date: 2018-05-31

Visual information, represented by various types of images and videos, is omnipresent, substantial, indispensable, diverse and complicated in our daily life. Regardless of being raw or processed, visual information is ultimately received and interpreted by our human beings. To assess the quality of images and videos, some traditional measures like the Peak Signal to Noise Ratio (PSNR) has been widely used. However, the inconsistency between these traditional measures and the human vision system (HVS) has hindered the development of visual information processing. Being aware of this problem, a large number of practitioners from the computer vision and image processing communities have focused on developing new metrics of visual quality assessment (VQA), which are designed perceptually more consistent to the HVS. In early research, they focused on imitating the HVS with the help of psychophysics. Then the trend in research became to treat the HVS as a black box and just imitate its functions. More recently, the practitioners start to exploit the links between statistics and the HVS, which were shaped and developed throughout the evolution of the HVS. In fact, the use of statistics, including the local and global summary statistics, statistical models and statistical machine learning techniques, becomes more and more popular in each constituent module of VQA, no matter there is reference information or not for assessment. In this context, this special issue aims to call for the state-of-the-art research in the technology, methodology, theory and application of VQA, especially the statistics-related aspects involved in VQA. It also aims to demonstrate the recent efforts made by the relevant researchers in the fields of computer vision, image processing, statistics and machine learning. We welcome all the relevant, original work, including but not limited to: - Statistics for natural scenes. - Statistics for specific types of image, such as screen content images. - Statistics for specific distortions of image. - Spatial and temporal statistics for videos. - Statistics-based perceptual features for VQA. - Statistical machine learning for VQA. - Deep learning for VQA. - Hybrid statistical and non-statistical learning for VQA. - Statistics-based pooling strategies in VQA. - Statistical evaluation of VQA methods. - Statistical analysis and interpretation of existing VQA methods. - VQA algorithms for image/video compression, denoising, restoration, enhancement, super-resolution, etc. - VQA applications in biometrics, medical imaging, remote sensing, security, etc. - VQA databases.
Last updated by Dou Sun in 2017-11-04
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