Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image Compression Article Swipe
Xuxiang Feng
,
Enjia Gu
,
Yongshan Zhang
,
An Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3462948
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3462948
Lossless remote sensing image compression aims to reduce the storage size of images without any information loss, ensuring that the decompressed image is identical to the original one. Most existing methods focus on lossy image compression that reduce the storage cost with certain data loss. It is challenging to perform lossless compression due to the very high-resolution images, long encoding–decoding time, and low compression efficiency. In this article, we propose a lossless compression framework that compresses remote sensing images in a coarse-to-fine manner. Specifically, checkerboard segmentation is applied on each image to generate six subimages from the main diagonal and counter-diagonal of each channel to maximally preserve the detail and structural information. The subimages from the main diagonal are initially compressed by a traditional compression method, while the subimages from the counter-diagonal are compressed channel by channel using our proposed probability prediction network (P2Net) and arithmetic coding with the previously encoded subimages from both the main diagonal and counter-diagonal as prior knowledge. The proposed P2Net consists of a upsampling module, a feature enhancement module, a downsampling module, and a probability prediction module to learn the discrete probability distribution of pixels. Lossless compression is conducted with arithmetic coding on the discrete probability distribution. To the best of our knowledge, this is the first deep learning-based lossless compression framework for three-channel remote sensing images. Experiments demonstrate that our framework outperforms the state-of-the-art methods and requires about 3.4 s to compress a 1024 3 image with 2.9% efficiency improvement compared to JPEG XL.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2024.3462948
- OA Status
- gold
- Cited By
- 3
- References
- 52
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402570051Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2024.3462948Digital Object Identifier
- Title
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Probability Prediction Network With Checkerboard Prior for Lossless Remote Sensing Image CompressionWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
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Xuxiang Feng, Enjia Gu, Yongshan Zhang, An LiList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2024.3462948Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/jstars.2024.3462948Direct OA link when available
- Concepts
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Checkerboard, Lossless compression, Computer science, Artificial intelligence, Data compression, Image compression, Lossy compression, Compression (physics), Computer vision, Image (mathematics), Pattern recognition (psychology), Image processing, Mathematics, Geometry, Materials science, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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52Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4396528656, https://openalex.org/W2889773939, https://openalex.org/W3093896170, https://openalex.org/W4399310774, https://openalex.org/W4399801011, https://openalex.org/W4399206013, https://openalex.org/W2899003677, https://openalex.org/W4401726115, https://openalex.org/W4294310684, https://openalex.org/W2594613658, https://openalex.org/W4323567911, https://openalex.org/W2914198047, https://openalex.org/W2393988244, https://openalex.org/W2965631471, https://openalex.org/W4312348190, https://openalex.org/W3135223238, https://openalex.org/W2140196014, https://openalex.org/W1998497852, https://openalex.org/W2963149687, https://openalex.org/W2981613960, https://openalex.org/W1877865836, https://openalex.org/W2971904947, https://openalex.org/W2032656843, https://openalex.org/W2908327824, https://openalex.org/W2783978965, https://openalex.org/W3107800305, https://openalex.org/W4366773133, https://openalex.org/W4386609485, https://openalex.org/W4375928920, https://openalex.org/W4390494417, https://openalex.org/W4401379732, https://openalex.org/W2887053471, https://openalex.org/W2915285784, https://openalex.org/W4396622366, https://openalex.org/W3208054474, https://openalex.org/W4320027769, https://openalex.org/W4399923941, https://openalex.org/W4223545469, https://openalex.org/W2066299746, https://openalex.org/W3163432535, https://openalex.org/W4200407972, https://openalex.org/W2126702571, https://openalex.org/W4206064231, https://openalex.org/W2044117333, https://openalex.org/W3049170458, https://openalex.org/W3089923941, https://openalex.org/W2103027412, https://openalex.org/W3175457126, https://openalex.org/W3034469748, https://openalex.org/W4385245566, https://openalex.org/W4206485136, https://openalex.org/W3037595032 |
| referenced_works_count | 52 |
| abstract_inverted_index.3 | 244 |
| abstract_inverted_index.a | 70, 80, 122, 167, 170, 174, 178, 238 |
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| abstract_inverted_index.by | 121, 135 |
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| abstract_inverted_index.XL. | 253 |
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| abstract_inverted_index.pixels. | 189 |
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| abstract_inverted_index.without | 13 |
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| abstract_inverted_index.efficiency | 248 |
| abstract_inverted_index.knowledge, | 207 |
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| abstract_inverted_index.prediction | 141, 180 |
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| abstract_inverted_index.structural | 110 |
| abstract_inverted_index.upsampling | 168 |
| abstract_inverted_index.2.9% | 247 |
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| abstract_inverted_index.challenging | 47 |
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| abstract_inverted_index.demonstrate | 223 |
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| abstract_inverted_index.Specifically, | 83 |
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| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.7861391 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |