Publication

Quantitative Evaluation of Dense Skeletons for Image Compression

Wang, J., Terpstra, M., Kosinka, J. & Telea, A., 20-May-2020, In : AHF-Information. 11, 5, 18 p., 274.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Wang, J., Terpstra, M., Kosinka, J., & Telea, A. (2020). Quantitative Evaluation of Dense Skeletons for Image Compression. AHF-Information, 11(5), [274]. https://doi.org/10.3390/info11050274

Author

Wang, Jieying ; Terpstra, Maarten ; Kosinka, Jiří ; Telea, Alexandru. / Quantitative Evaluation of Dense Skeletons for Image Compression. In: AHF-Information. 2020 ; Vol. 11, No. 5.

Harvard

Wang, J, Terpstra, M, Kosinka, J & Telea, A 2020, 'Quantitative Evaluation of Dense Skeletons for Image Compression', AHF-Information, vol. 11, no. 5, 274. https://doi.org/10.3390/info11050274

Standard

Quantitative Evaluation of Dense Skeletons for Image Compression. / Wang, Jieying; Terpstra, Maarten; Kosinka, Jiří; Telea, Alexandru.

In: AHF-Information, Vol. 11, No. 5, 274, 20.05.2020.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Wang J, Terpstra M, Kosinka J, Telea A. Quantitative Evaluation of Dense Skeletons for Image Compression. AHF-Information. 2020 May 20;11(5). 274. https://doi.org/10.3390/info11050274


BibTeX

@article{350091dc22914fc393378b0b5953b049,
title = "Quantitative Evaluation of Dense Skeletons for Image Compression",
abstract = "Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. In this paper, we fill this gap with two main contributions. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. We use this benchmark to derive optimal parameters for dense skeletons. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding.",
author = "Jieying Wang and Maarten Terpstra and Jiř{\'i} Kosinka and Alexandru Telea",
year = "2020",
month = "5",
day = "20",
doi = "10.3390/info11050274",
language = "English",
volume = "11",
journal = "AHF-Information",
issn = "2078-2489",
publisher = "MDPI AG",
number = "5",

}

RIS

TY - JOUR

T1 - Quantitative Evaluation of Dense Skeletons for Image Compression

AU - Wang, Jieying

AU - Terpstra, Maarten

AU - Kosinka, Jiří

AU - Telea, Alexandru

PY - 2020/5/20

Y1 - 2020/5/20

N2 - Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. In this paper, we fill this gap with two main contributions. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. We use this benchmark to derive optimal parameters for dense skeletons. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding.

AB - Skeletons are well-known descriptors used for analysis and processing of 2D binary images. Recently, dense skeletons have been proposed as an extension of classical skeletons as a dual encoding for 2D grayscale and color images. Yet, their encoding power, measured by the quality and size of the encoded image, and how these metrics depend on selected encoding parameters, has not been formally evaluated. In this paper, we fill this gap with two main contributions. First, we improve the encoding power of dense skeletons by effective layer selection heuristics, a refined skeleton pixel-chain encoding, and a postprocessing compression scheme. Secondly, we propose a benchmark to assess the encoding power of dense skeletons for a wide set of natural and synthetic color and grayscale images. We use this benchmark to derive optimal parameters for dense skeletons. Our method, called Compressing Dense Medial Descriptors (CDMD), achieves higher-compression ratios at similar quality to the well-known JPEG technique and, thereby, shows that skeletons can be an interesting option for lossy image encoding.

U2 - 10.3390/info11050274

DO - 10.3390/info11050274

M3 - Article

VL - 11

JO - AHF-Information

JF - AHF-Information

SN - 2078-2489

IS - 5

M1 - 274

ER -

ID: 125438462