Quantitative Evaluation of Dense Skeletons for Image Compression
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 enco...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/11/5/274 |
| _version_ | 1797567492041736192 |
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| author | Jieying Wang Maarten Terpstra Jiří Kosinka Alexandru Telea |
| author_facet | Jieying Wang Maarten Terpstra Jiří Kosinka Alexandru Telea |
| author_sort | Jieying Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:42:48Z |
| format | Article |
| id | doaj.art-67ff5d944ba04875b76d95c54b2b7654 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T19:42:48Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-67ff5d944ba04875b76d95c54b2b76542023-11-20T01:02:45ZengMDPI AGInformation2078-24892020-05-0111527410.3390/info11050274Quantitative Evaluation of Dense Skeletons for Image CompressionJieying Wang0Maarten Terpstra1Jiří Kosinka2Alexandru Telea3Bernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsBernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsBernoulli Institute, University of Groningen, 9747 AG Groningen, The NetherlandsDepartment of Information and Computing Sciences, Utrecht University, 3584 CC Utrecht, The NetherlandsSkeletons 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.https://www.mdpi.com/2078-2489/11/5/274medial descriptorsskeletonizationimage compressionbenchmarking |
| spellingShingle | Jieying Wang Maarten Terpstra Jiří Kosinka Alexandru Telea Quantitative Evaluation of Dense Skeletons for Image Compression Information medial descriptors skeletonization image compression benchmarking |
| title | Quantitative Evaluation of Dense Skeletons for Image Compression |
| title_full | Quantitative Evaluation of Dense Skeletons for Image Compression |
| title_fullStr | Quantitative Evaluation of Dense Skeletons for Image Compression |
| title_full_unstemmed | Quantitative Evaluation of Dense Skeletons for Image Compression |
| title_short | Quantitative Evaluation of Dense Skeletons for Image Compression |
| title_sort | quantitative evaluation of dense skeletons for image compression |
| topic | medial descriptors skeletonization image compression benchmarking |
| url | https://www.mdpi.com/2078-2489/11/5/274 |
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