Journal Information
Signal Processing: Image Communication
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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 Deep Learning in Image and Video Forensics
Submission Date: 2017-10-02

The pervasiveness of new technologies, such as smartphones, tablets and Internet made digital images and videos the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing software that can easily alter the original content without any visual trace of the modification. Nowadays, machine learning techniques and, in particular, Deep Learning have come to play a vital role to deal with a massive amount of unsupervised data. In recent years, deep neural networks, such as deep belief network, deep autoencoder and convolutional neural network (CNN), have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classification, speech recognition and so on. The extensive use of Deep Learning in many areas has motivated and led the multimedia forensics community to comprehend if such technological solution is able to detect image and video manipulations or to exploit source identification. For example, it has been foreseen the proposal of new convolutional network architecture capable of working on any kind of different image formats and of automatically learning manipulation detection features directly from the training data itself. A general data-driven forensics methodology should be devised to accomplish the forensics tasks, independent from the kind of tampering, from the image format and designed to detect many, if not all, editing operations. Furthermore, it is interesting to investigate the adversarial actions performed on deep learning techniques, to understand how their analysis can be biased and perturbed by means of the injection of fake data or adversarial examples and how the trustworthiness of the produced knowledge is diminished in relation with the kind and intensity of the performed manipulation (e.g. forged images and videos). The aim of this special issue is to gather image forensic works specifically oriented to deal with deep learning based approach with applications in passive image forensics. We solicit high-quality original research papers as well as review papers that mainly address these issues and advance the development in image forensics. Submitted papers should not be previously published or be under consideration for publication elsewhere. Potential topics include, but are not limited to: - tampering detection machine learning classification - deep learning tampering detection - source identification with deep learning - relationship between adversarial forensics and deep learning
Last updated by Dou Sun in 2017-06-18
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
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