Call for Papers

CFP: Signal Processing: Image Communication Special Issue on
Advances in Statistical Methods-based Visual Quality Assessment
Submission Deadline: May 31, 2018.
https://www.journals.elsevier.com/signal-processing-image-communication/call-for-papers


Signal Processing: Image Communication
Special Issue on 
Advances in Statistical Methods-based Visual Quality Assessment

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.

Important dates
Manuscript submission: 31 May 2018
First notification: 31 August 2018
Revision: 31 October 2018
Final decision: 30 November 2018
Tentative publication date: February 2019

Guest editors
* Dr Fei Zhou, Tsinghua University, China
* Prof Wenming Yang, Tsinghua University, China
* Dr Hantao Liu, Cardiff University, UK
* Dr Rui Zhu, University of Kent, UK
* Prof Xinbo Gao, Xidian University, China
* Dr Jing-Hao Xue, University College London, UK