Example of Signal, Image and Video Processing format
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open access Open Access

Signal, Image and Video Processing — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Electrical and Electronic Engineering #239 of 693 down down by 25 ranks
Signal Processing #44 of 108 down down by 1 rank
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 747 Published Papers | 2873 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/07/2020
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FAQ

Related Journals

open access Open Access

Hindawi

Quality:  
High
CiteRatio: 3.9
SJR: 0.318
SNIP: 0.926
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 7.3
SJR: 0.815
SNIP: 1.797
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 5.5
SJR: 0.502
SNIP: 1.247

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.794

5% from 2018

Impact factor for Signal, Image and Video Processing from 2016 - 2019
Year Value
2019 1.794
2018 1.894
2017 1.643
2016 1.102
graph view Graph view
table view Table view

3.8

CiteRatio for Signal, Image and Video Processing from 2016 - 2020
Year Value
2020 3.8
2019 3.8
2018 3.7
2017 3.0
2016 2.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 5% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.409

14% from 2019

SJR for Signal, Image and Video Processing from 2016 - 2020
Year Value
2020 0.409
2019 0.478
2018 0.501
2017 0.485
2016 0.373
graph view Graph view
table view Table view

0.942

17% from 2019

SNIP for Signal, Image and Video Processing from 2016 - 2020
Year Value
2020 0.942
2019 1.135
2018 1.269
2017 1.227
2016 1.117
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 14% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 17% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Signal, Image and Video Processing

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Springer

Signal, Image and Video Processing

Aims & Scope The journal is an interdisciplinary journal presenting the theory and practice of Signal, Image and Video Processing.  It aims at: • Disseminating high level research results and engineering developments to all Signal, Image or Video Processing researchers and res...... Read More

Engineering

i
Last updated on
19 Jul 2020
i
ISSN
1863-1703
i
Impact Factor
Medium - 0.874
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1007/S11760-013-0556-9
Image fusion based on pixel significance using cross bilateral filter

Abstract:

Like bilateral filter (BF), cross bilateral filter (CBF) considers both gray-level similarities and geometric closeness of the neighboring pixels without smoothing edges, but it uses one image for finding the kernel and other to filter, and vice versa. In this paper, it is proposed to fuse source images by weighted average us... Like bilateral filter (BF), cross bilateral filter (CBF) considers both gray-level similarities and geometric closeness of the neighboring pixels without smoothing edges, but it uses one image for finding the kernel and other to filter, and vice versa. In this paper, it is proposed to fuse source images by weighted average using the weights computed from the detail images that are extracted from the source images using CBF. The performance of the proposed method has been verified on several pairs of multisensor and multifocus images and compared with the existing methods visually and quantitatively. It is found that, none of the methods have shown consistence performance for all the performance metrics. But as compared to them, the proposed method has shown good performance in most of the cases. Further, the visual quality of the fused image by the proposed method is superior to other methods. read more read less

Topics:

Bilateral filter (69%)69% related to the paper, Image fusion (56%)56% related to the paper, Pixel (53%)53% related to the paper, Smoothing (52%)52% related to the paper, Kernel (image processing) (50%)50% related to the paper
417 Citations
Journal Article DOI: 10.1007/S11760-010-0204-6
A survey on super-resolution imaging
Jing Tian1, Kai-Kuang Ma1

Abstract:

The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and... The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of SR image and video reconstruction methods developed in the literature and highlight the future research challenges. The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images, and the SR video approaches reconstruct an image sequence with a higher-resolution from a group of adjacent lower-resolution image frames. Furthermore, several SR applications are discussed to contribute some insightful comments on future SR research directions. Specifically, the SR computations for multi-view images and the SR video computation in the temporal domain are discussed. read more read less
255 Citations
open accessOpen access Journal Article DOI: 10.1007/S11760-017-1166-8
On the use of deep learning for blind image quality assessment
Simone Bianco1, Luigi Celona1, Paolo Napoletano1, Raimondo Schettini1

Abstract:

In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-t... In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013. read more read less

Topics:

Image quality (62%)62% related to the paper, Convolutional neural network (53%)53% related to the paper, Deep learning (53%)53% related to the paper
View PDF
254 Citations
Journal Article DOI: 10.1007/S11760-012-0361-X
Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform
B. K. Shreyamsha Kumar1

Abstract:

The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavel... The energy compaction and multiresolution properties of wavelets have made the image fusion successful in combining important features such as edges and textures from source images without introducing any artifacts for context enhancement and situational awareness. The wavelet transform is visualized as a convolution of wavelet filter coefficients with the image under consideration and is computationally intensive. The advent of lifting-based wavelets has reduced the computations but at the cost of visual quality and performance of the fused image. To retain the visual quality and performance of the fused image with reduced computations, a discrete cosine harmonic wavelet (DCHWT)-based image fusion is proposed. The performance of DCHWT is compared with both convolution and lifting-based image fusion approaches. It is found that the performance of DCHWT is similar to convolution-based wavelets and superior/similar to lifting-based wavelets. Also, the computational complexity (in terms of additions and multiplications) of the proposed method scores over convolution-based wavelets and is competitive to lifting-based wavelets. read more read less

Topics:

Discrete wavelet transform (68%)68% related to the paper, Lifting scheme (68%)68% related to the paper, Wavelet (67%)67% related to the paper, Gabor wavelet (65%)65% related to the paper, Fast wavelet transform (63%)63% related to the paper
234 Citations
Journal Article DOI: 10.1007/S11760-012-0362-9
Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network
Yatindra Kumar1, M. L. Dewal1, R. S. Anand1

Abstract:

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A sc... There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique. read more read less

Topics:

Electroencephalography (55%)55% related to the paper, Epilepsy (53%)53% related to the paper
224 Citations
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Signal, Image and Video Processing format uses SPBASIC citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

1. Can I write Signal, Image and Video Processing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Signal, Image and Video Processing guidelines and auto format it.

2. Do you follow the Signal, Image and Video Processing guidelines?

Yes, the template is compliant with the Signal, Image and Video Processing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Signal, Image and Video Processing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Signal, Image and Video Processing citation style.

4. Can I use the Signal, Image and Video Processing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Signal, Image and Video Processing.

5. Can I use a manuscript in Signal, Image and Video Processing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Signal, Image and Video Processing that you can download at the end.

6. How long does it usually take you to format my papers in Signal, Image and Video Processing?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Signal, Image and Video Processing.

7. Where can I find the template for the Signal, Image and Video Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Signal, Image and Video Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Signal, Image and Video Processing's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Signal, Image and Video Processing an online tool or is there a desktop version?

SciSpace's Signal, Image and Video Processing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Signal, Image and Video Processing?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Signal, Image and Video Processing?”

11. What is the output that I would get after using Signal, Image and Video Processing?

After writing your paper autoformatting in Signal, Image and Video Processing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Signal, Image and Video Processing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Signal, Image and Video Processing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Signal, Image and Video Processing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Signal, Image and Video Processing?

The 5 most common citation types in order of usage for Signal, Image and Video Processing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Signal, Image and Video Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Signal, Image and Video Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Signal, Image and Video Processing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Signal, Image and Video Processing Endnote style according to Elsevier guidelines.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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