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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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 HIGHLY CITED PAPER 

Lossy Compression using Adaptive Polynomial Image Encoding

OTHMAN, S. See more information about OTHMAN, S. on SCOPUS See more information about OTHMAN, S. on IEEExplore See more information about OTHMAN, S. on Web of Science, MOHAMED, A. See more information about  MOHAMED, A. on SCOPUS See more information about  MOHAMED, A. on SCOPUS See more information about MOHAMED, A. on Web of Science, ABOUALI, A. See more information about  ABOUALI, A. on SCOPUS See more information about  ABOUALI, A. on SCOPUS See more information about ABOUALI, A. on Web of Science, NOSSAIR, Z. See more information about NOSSAIR, Z. on SCOPUS See more information about NOSSAIR, Z. on SCOPUS See more information about NOSSAIR, Z. on Web of Science
 
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Download PDF pdficon (1,465 KB) | Citation | Downloads: 952 | Views: 2,262

Author keywords
adaptive, compression, decoding, encoding, polynomial

References keywords
image(36), comput(11), techniques(9), encoding(8), technol(7), coding(7), fitting(6), algorithm(6), technology(5), survey(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-02-28
Volume 21, Issue 1, Year 2021, On page(s): 91 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.01010
Web of Science Accession Number: 000624018800010
SCOPUS ID: 85102848184

Abstract
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In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the number of data elements in an image block to a few coefficients. The proposed approach consists of two processes: encoding and decoding. In the encoding process, the coefficient matrix is created by representing each block of the image with a first- or second-order two-dimensional polynomial. The encoded block size of the image is variable. The polynomial order and the encoded block size are determined dynamically depending on the value of a threshold. A prefix code of two bits is used to differentiate the encoding states. Uniform quantization is applied to the coefficient matrix to store these coefficients effectively. In the decoding process, the reconstructed (decompressed) image is built from the quantized coefficient matrix. The fitting variables are two-dimensional (x, y). The encoding and decoding processes require a single image scan without the need to transfer the matrix to another domain. Experimentally, a high compression ratio is achieved at an acceptable quality for both gray and color images. The results are comparable to those of most recent studies.


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References Weight

Web of Science® Citations for all references: 242 TCR
SCOPUS® Citations for all references: 453 TCR

Web of Science® Average Citations per reference: 5 ACR
SCOPUS® Average Citations per reference: 10 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-12-02 18:42 in 276 seconds.




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