Two-Dimensional Image Lempel–Ziv Complexity Calculation Method and Its Application in Defect Detection Article Swipe
Jiancheng Yin
,
Wentao Sui
,
Xuye Zhuang
,
Yunlong Sheng
,
Yongbo Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/e27101014
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/e27101014
Although Lempel–Ziv complexity (LZC) can reflect changes in object characteristics by measuring changes in independent patterns in the signal, it can only be applied to one-dimensional time series and cannot be directly applied to two-dimensional images. To address this issue, this paper proposed a two-dimensional Lempel–Ziv complexity by combining the concept of local receptive field in convolutional neural networks. This extends the application scenario of LZC from one-dimensional time series to two-dimensional images, further broadening the scope of application of LZC. First, the pixels and size of the image were normalized. Then, the image was encoded according to the sorting of normalized values within the 4 × 4 region. Next, the encoding result of the image was rearranged into a vector by row. Finally, the Lempel–Ziv complexity of the image could be obtained based on the rearranged vector. The proposed method was further used for defect detection in conjunction with the dilation operator and Sobel operator, and validated by two practical cases. The results showed that the proposed method can effectively identify independent pattern changes in images and can be used for defect detection. The accuracy rate of defect detection can reach 100%.
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- article
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- https://doi.org/10.3390/e27101014
- https://www.mdpi.com/1099-4300/27/10/1014/pdf?version=1758965473
- OA Status
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- 61
- OpenAlex ID
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https://openalex.org/W4414588675Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/e27101014Digital Object Identifier
- Title
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Two-Dimensional Image Lempel–Ziv Complexity Calculation Method and Its Application in Defect DetectionWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-09-27Full publication date if available
- Authors
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Jiancheng Yin, Wentao Sui, Xuye Zhuang, Yunlong Sheng, Yongbo LiList of authors in order
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https://www.mdpi.com/1099-4300/27/10/1014/pdf?version=1758965473Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1099-4300/27/10/1014/pdf?version=1758965473Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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61Number of works referenced by this work
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| abstract_inverted_index.This | 59 |
| abstract_inverted_index.from | 66 |
| abstract_inverted_index.into | 118 |
| abstract_inverted_index.only | 21 |
| abstract_inverted_index.rate | 186 |
| abstract_inverted_index.row. | 122 |
| abstract_inverted_index.size | 85 |
| abstract_inverted_index.that | 165 |
| abstract_inverted_index.this | 38, 40 |
| abstract_inverted_index.time | 26, 68 |
| abstract_inverted_index.used | 143, 180 |
| abstract_inverted_index.were | 89 |
| abstract_inverted_index.with | 149 |
| abstract_inverted_index.(LZC) | 3 |
| abstract_inverted_index.100%. | 192 |
| abstract_inverted_index.Next, | 109 |
| abstract_inverted_index.Sobel | 154 |
| abstract_inverted_index.Then, | 91 |
| abstract_inverted_index.based | 133 |
| abstract_inverted_index.could | 130 |
| abstract_inverted_index.field | 54 |
| abstract_inverted_index.image | 88, 93, 115, 129 |
| abstract_inverted_index.local | 52 |
| abstract_inverted_index.paper | 41 |
| abstract_inverted_index.reach | 191 |
| abstract_inverted_index.scope | 76 |
| abstract_inverted_index.First, | 81 |
| abstract_inverted_index.cannot | 29 |
| abstract_inverted_index.cases. | 161 |
| abstract_inverted_index.defect | 145, 182, 188 |
| abstract_inverted_index.images | 176 |
| abstract_inverted_index.issue, | 39 |
| abstract_inverted_index.method | 140, 168 |
| abstract_inverted_index.neural | 57 |
| abstract_inverted_index.object | 8 |
| abstract_inverted_index.pixels | 83 |
| abstract_inverted_index.result | 112 |
| abstract_inverted_index.series | 27, 69 |
| abstract_inverted_index.showed | 164 |
| abstract_inverted_index.values | 102 |
| abstract_inverted_index.vector | 120 |
| abstract_inverted_index.within | 103 |
| abstract_inverted_index.address | 37 |
| abstract_inverted_index.applied | 23, 32 |
| abstract_inverted_index.changes | 6, 12, 174 |
| abstract_inverted_index.concept | 50 |
| abstract_inverted_index.encoded | 95 |
| abstract_inverted_index.extends | 60 |
| abstract_inverted_index.further | 73, 142 |
| abstract_inverted_index.images, | 72 |
| abstract_inverted_index.images. | 35 |
| abstract_inverted_index.pattern | 173 |
| abstract_inverted_index.reflect | 5 |
| abstract_inverted_index.region. | 108 |
| abstract_inverted_index.results | 163 |
| abstract_inverted_index.signal, | 18 |
| abstract_inverted_index.sorting | 99 |
| abstract_inverted_index.vector. | 137 |
| abstract_inverted_index.Although | 0 |
| abstract_inverted_index.Finally, | 123 |
| abstract_inverted_index.accuracy | 185 |
| abstract_inverted_index.dilation | 151 |
| abstract_inverted_index.directly | 31 |
| abstract_inverted_index.encoding | 111 |
| abstract_inverted_index.identify | 171 |
| abstract_inverted_index.obtained | 132 |
| abstract_inverted_index.operator | 152 |
| abstract_inverted_index.patterns | 15 |
| abstract_inverted_index.proposed | 42, 139, 167 |
| abstract_inverted_index.scenario | 63 |
| abstract_inverted_index.according | 96 |
| abstract_inverted_index.combining | 48 |
| abstract_inverted_index.detection | 146, 189 |
| abstract_inverted_index.measuring | 11 |
| abstract_inverted_index.networks. | 58 |
| abstract_inverted_index.operator, | 155 |
| abstract_inverted_index.practical | 160 |
| abstract_inverted_index.receptive | 53 |
| abstract_inverted_index.validated | 157 |
| abstract_inverted_index.broadening | 74 |
| abstract_inverted_index.complexity | 2, 46, 126 |
| abstract_inverted_index.detection. | 183 |
| abstract_inverted_index.normalized | 101 |
| abstract_inverted_index.rearranged | 117, 136 |
| abstract_inverted_index.application | 62, 78 |
| abstract_inverted_index.conjunction | 148 |
| abstract_inverted_index.effectively | 170 |
| abstract_inverted_index.independent | 14, 172 |
| abstract_inverted_index.normalized. | 90 |
| abstract_inverted_index.Lempel–Ziv | 1, 45, 125 |
| abstract_inverted_index.convolutional | 56 |
| abstract_inverted_index.characteristics | 9 |
| abstract_inverted_index.one-dimensional | 25, 67 |
| abstract_inverted_index.two-dimensional | 34, 44, 71 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5053360857, https://openalex.org/A5100665121 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I119203015, https://openalex.org/I17145004 |
| citation_normalized_percentile.value | 0.59660285 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |