Example of Signal Processing: Image Communication format
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Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format
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Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format Example of Signal Processing: Image Communication format
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open access Open Access

Signal Processing: Image Communication — Template for authors

Publisher: Elsevier
Categories Rank Trend in last 3 yrs
Electrical and Electronic Engineering #122 of 693 down down by 6 ranks
Software #89 of 389 up up by 8 ranks
Signal Processing #27 of 108 down down by 5 ranks
Computer Vision and Pattern Recognition #22 of 85 down down by 5 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 644 Published Papers | 4053 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/07/2020
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Related Journals

open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 9.1
SJR: 0.907
SNIP: 1.713
open access Open Access

IET Publications

Quality:  
Good
CiteRatio: 3.2
SJR: 0.401
SNIP: 1.167
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recommended Recommended

IEEE

Quality:  
High
CiteRatio: 11.4
SJR: 1.005
SNIP: 2.547
open access Open Access
recommended Recommended

Elsevier

Quality:  
High
CiteRatio: 15.7
SJR: 1.492
SNIP: 3.419

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

6.3

13% from 2019

CiteRatio for Signal Processing: Image Communication from 2016 - 2020
Year Value
2020 6.3
2019 5.6
2018 5.2
2017 4.6
2016 4.9
graph view Graph view
table view Table view

0.544

20% from 2019

SJR for Signal Processing: Image Communication from 2016 - 2020
Year Value
2020 0.544
2019 0.679
2018 0.562
2017 0.551
2016 0.592
graph view Graph view
table view Table view

1.494

12% from 2019

SNIP for Signal Processing: Image Communication from 2016 - 2020
Year Value
2020 1.494
2019 1.698
2018 1.699
2017 1.512
2016 1.546
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 13% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has decreased by 20% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 12% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Signal Processing: Image Communication

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Elsevier

Signal Processing: Image Communication

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 stimulat...... Read More

Engineering

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Last updated on
11 Jul 2020
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ISSN
0923-5965
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Impact Factor
High - 1.444
i
Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
elsarticle-num
i
Citation Type
Numbered
[25]
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Bibliography Example
G. E. Blonder, M. Tinkham, T. M. Klapwijk, Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion, Phys. Rev. B 25 (7) (1982) 4515–4532. URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

Journal Article DOI: 10.1016/S0923-5965(03)00076-6
Video Quality Assessment Based on Structural Distortion Measurement
Zhou Wang1, Zhou Wang2, Ligang Lu3, Alan C. Bovik4

Abstract:

Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based... Objective image and video quality measures play important roles in a variety of image and video pro- cessing applications, such as compression, communication, printing, analysis, registration, restoration, enhancement and watermarking. Most proposed quality assessment ap- proaches in the literature are error sensitivity-based meth- ods. In this paper, we follow a new philosophy in designing image and video quality metrics, which uses structural dis- tortion as an estimate of perceived visual distortion. A com- putationally ecient approach is developed for full-reference (FR) video quality assessment. The algorithm is tested on the video quality experts group (VQEG) Phase I FR-TV test data set. Keywords—Image quality assessment, video quality assess- ment, human visual system, error sensitivity, structural dis- tortion, video quality experts group (VQEG) read more read less

Topics:

Subjective video quality (71%)71% related to the paper, Video quality (70%)70% related to the paper, PEVQ (67%)67% related to the paper, Image quality (58%)58% related to the paper, Human visual system model (56%)56% related to the paper
View PDF
1,083 Citations
open accessOpen access Journal Article DOI: 10.1016/J.IMAGE.2014.10.009
Image database TID2013

Abstract:

This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 level... This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described. read more read less
View PDF
943 Citations
Journal Article DOI: 10.1016/J.IMAGE.2014.06.006
No-reference image quality assessment based on spatial and spectral entropies
Lixiong Liu1, Bao Liu1, Hua Huang1, Alan C. Bovik2

Abstract:

We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features on distorted images. Using a 2-stage framework of distortion classification followed by quality assessment, we utilize a support vector machine (SVM) to train an image distor... We develop an efficient general-purpose no-reference (NR) image quality assessment (IQA) model that utilizes local spatial and spectral entropy features on distorted images. Using a 2-stage framework of distortion classification followed by quality assessment, we utilize a support vector machine (SVM) to train an image distortion and quality prediction engine. The resulting algorithm, dubbed Spatial–Spectral Entropy-based Quality (SSEQ) index, is capable of assessing the quality of a distorted image across multiple distortion categories. We explain the entropy features used and their relevance to perception and thoroughly evaluate the algorithm on the LIVE IQA database. We find that SSEQ matches well with human subjective opinions of image quality, and is statistically superior to the full-reference (FR) IQA algorithm SSIM and several top-performing NR IQA methods: BIQI, DIIVINE, and BLIINDS-II. SSEQ has a considerably low complexity. We also tested SSEQ on the TID2008 database to ascertain whether it has performance that is database independent. read more read less

Topics:

Image quality (56%)56% related to the paper
562 Citations
Journal Article DOI: 10.1016/S0923-5965(02)00084-X
Shape-based image retrieval using generic Fourier descriptor
Dengsheng Zhang1, Guojun Lu1

Abstract:

Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbac... Shape description is one of the key parts of image content description for image retrieval. Most of the existing shape descriptors are usually either application dependent or non-robust, making them undesirable for generic shape description. In this paper, a generic Fourier descriptor (GFD) is proposed to overcome the drawbacks of existing shape representation techniques. The proposed shape descriptor is derived by applying two-dimensional Fourier transform on a polar-raster sampled shape image. The acquired shape descriptor is application independent and robust. Experimental results show that the proposed GFD outperforms common contour-based and region-based shape descriptors. read more read less

Topics:

Active shape model (65%)65% related to the paper, Heat kernel signature (65%)65% related to the paper, Shape analysis (digital geometry) (61%)61% related to the paper, Image retrieval (54%)54% related to the paper, Image processing (53%)53% related to the paper
534 Citations
Journal Article DOI: 10.1016/S0923-5965(01)00024-8
An overview of the JPEG 2000 still image compression standard
Majid Rabbani1, Rajan L. Joshi1

Abstract:

In 1996, the JPEG committee began to investigate possibilities for a new still image compression standard to serve current and future applications. This initiative, which was named JPEG 2000, has resulted in a comprehensive standard (ISO 15444∣ITU-T Recommendation T.800) that is being issued in six parts. Part 1, in the same ... In 1996, the JPEG committee began to investigate possibilities for a new still image compression standard to serve current and future applications. This initiative, which was named JPEG 2000, has resulted in a comprehensive standard (ISO 15444∣ITU-T Recommendation T.800) that is being issued in six parts. Part 1, in the same vein as the JPEG baseline system, is aimed at minimal complexity and maximal interchange and was issued as an International Standard at the end of 2000. Parts 2–6 define extensions to both the compression technology and the file format and are currently in various stages of development. In this paper, a technical description of Part 1 of the JPEG 2000 standard is provided, and the rationale behind the selected technologies is explained. Although the JPEG 2000 standard only specifies the decoder and the codesteam syntax, the discussion will span both encoder and decoder issues to provide a better understanding of the standard in various applications. read more read less

Topics:

JPEG File Interchange Format (75%)75% related to the paper, JPEG 2000 (68%)68% related to the paper, JPEG (64%)64% related to the paper, File format (53%)53% related to the paper, Image compression (51%)51% related to the paper
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528 Citations
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Signal Processing: Image Communication format uses elsarticle-num citation style.

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Frequently asked questions

1. Can I write Signal Processing: Image Communication 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 Processing: Image Communication guidelines and auto format it.

2. Do you follow the Signal Processing: Image Communication guidelines?

Yes, the template is compliant with the Signal Processing: Image Communication 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 Processing: Image Communication?

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 Processing: Image Communication citation style.

4. Can I use the Signal Processing: Image Communication 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 Processing: Image Communication.

5. Can I use a manuscript in Signal Processing: Image Communication 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 Processing: Image Communication that you can download at the end.

6. How long does it usually take you to format my papers in Signal Processing: Image Communication?

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 Processing: Image Communication.

7. Where can I find the template for the Signal Processing: Image Communication?

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 Processing: Image Communication'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 Processing: Image Communication'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 Processing: Image Communication an online tool or is there a desktop version?

SciSpace's Signal Processing: Image Communication 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 Processing: Image Communication?

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 Processing: Image Communication?”

11. What is the output that I would get after using Signal Processing: Image Communication?

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

12. Is Signal Processing: Image Communication'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 Processing: Image Communication?

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 Processing: Image Communication. 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 Processing: Image Communication?

The 5 most common citation types in order of usage for Signal Processing: Image Communication 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 Processing: Image Communication?

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 Processing: Image Communication's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Signal Processing: Image Communication 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 Processing: Image Communication Endnote style according to Elsevier guidelines.

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