High Frequency Matters: Uncertainty Guided Image Compression with Wavelet Diffusion Article Swipe
Juan Song
,
Jiaxiang He
,
Mingtao Feng
,
Keyan Wang
,
Yunsong Li
,
Ajmal Mian
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.12538
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.12538
Diffusion probabilistic models have recently achieved remarkable success in generating high-quality images. However, balancing high perceptual quality and low distortion remains challenging in image compression applications. To address this issue, we propose an efficient Uncertainty-Guided image compression approach with wavelet Diffusion (UGDiff). Our approach focuses on high frequency compression via the wavelet transform, since high frequency components are crucial for reconstructing image details. We introduce a wavelet conditional diffusion model for high frequency prediction, followed by a residual codec that compresses and transmits prediction residuals to the decoder. This diffusion prediction-then-residual compression paradigm effectively addresses the low fidelity issue common in direct reconstructions by existing diffusion models. Considering the uncertainty from the random sampling of the diffusion model, we further design an uncertainty-weighted rate-distortion (R-D) loss tailored for residual compression, providing a more rational trade-off between rate and distortion. Comprehensive experiments on two benchmark datasets validate the effectiveness of UGDiff, surpassing state-of-the-art image compression methods in R-D performance, perceptual quality, subjective quality, and inference time. Our code is available at: https://github.com/hejiaxiang1/Wavelet-Diffusion/tree/main
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.12538
- https://arxiv.org/pdf/2407.12538
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403750494
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403750494Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2407.12538Digital Object Identifier
- Title
-
High Frequency Matters: Uncertainty Guided Image Compression with Wavelet DiffusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-17Full publication date if available
- Authors
-
Juan Song, Jiaxiang He, Mingtao Feng, Keyan Wang, Yunsong Li, Ajmal MianList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.12538Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.12538Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2407.12538Direct OA link when available
- Concepts
-
Wavelet, Image (mathematics), Image compression, Diffusion, Compression (physics), Computer science, Artificial intelligence, Computer vision, Econometrics, Mathematics, Image processing, Materials science, Physics, Thermodynamics, Composite materialTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403750494 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2407.12538 |
| ids.doi | https://doi.org/10.48550/arxiv.2407.12538 |
| ids.openalex | https://openalex.org/W4403750494 |
| fwci | |
| type | preprint |
| title | High Frequency Matters: Uncertainty Guided Image Compression with Wavelet Diffusion |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10688 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9532999992370605 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Image and Signal Denoising Methods |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C47432892 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6504483819007874 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q831390 |
| concepts[0].display_name | Wavelet |
| concepts[1].id | https://openalex.org/C115961682 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5461084842681885 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[1].display_name | Image (mathematics) |
| concepts[2].id | https://openalex.org/C13481523 |
| concepts[2].level | 4 |
| concepts[2].score | 0.48695454001426697 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q412438 |
| concepts[2].display_name | Image compression |
| concepts[3].id | https://openalex.org/C69357855 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4539307653903961 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q163214 |
| concepts[3].display_name | Diffusion |
| concepts[4].id | https://openalex.org/C180016635 |
| concepts[4].level | 2 |
| concepts[4].score | 0.44726836681365967 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2712821 |
| concepts[4].display_name | Compression (physics) |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.39733582735061646 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.36995041370391846 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C31972630 |
| concepts[7].level | 1 |
| concepts[7].score | 0.35079225897789 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[7].display_name | Computer vision |
| concepts[8].id | https://openalex.org/C149782125 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3479135036468506 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[8].display_name | Econometrics |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.3051045536994934 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C9417928 |
| concepts[10].level | 3 |
| concepts[10].score | 0.23295792937278748 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1070689 |
| concepts[10].display_name | Image processing |
| concepts[11].id | https://openalex.org/C192562407 |
| concepts[11].level | 0 |
| concepts[11].score | 0.173365980386734 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[11].display_name | Materials science |
| concepts[12].id | https://openalex.org/C121332964 |
| concepts[12].level | 0 |
| concepts[12].score | 0.16998502612113953 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[12].display_name | Physics |
| concepts[13].id | https://openalex.org/C97355855 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[13].display_name | Thermodynamics |
| concepts[14].id | https://openalex.org/C159985019 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q181790 |
| concepts[14].display_name | Composite material |
| keywords[0].id | https://openalex.org/keywords/wavelet |
| keywords[0].score | 0.6504483819007874 |
| keywords[0].display_name | Wavelet |
| keywords[1].id | https://openalex.org/keywords/image |
| keywords[1].score | 0.5461084842681885 |
| keywords[1].display_name | Image (mathematics) |
| keywords[2].id | https://openalex.org/keywords/image-compression |
| keywords[2].score | 0.48695454001426697 |
| keywords[2].display_name | Image compression |
| keywords[3].id | https://openalex.org/keywords/diffusion |
| keywords[3].score | 0.4539307653903961 |
| keywords[3].display_name | Diffusion |
| keywords[4].id | https://openalex.org/keywords/compression |
| keywords[4].score | 0.44726836681365967 |
| keywords[4].display_name | Compression (physics) |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.39733582735061646 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.36995041370391846 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/computer-vision |
| keywords[7].score | 0.35079225897789 |
| keywords[7].display_name | Computer vision |
| keywords[8].id | https://openalex.org/keywords/econometrics |
| keywords[8].score | 0.3479135036468506 |
| keywords[8].display_name | Econometrics |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.3051045536994934 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/image-processing |
| keywords[10].score | 0.23295792937278748 |
| keywords[10].display_name | Image processing |
| keywords[11].id | https://openalex.org/keywords/materials-science |
| keywords[11].score | 0.173365980386734 |
| keywords[11].display_name | Materials science |
| keywords[12].id | https://openalex.org/keywords/physics |
| keywords[12].score | 0.16998502612113953 |
| keywords[12].display_name | Physics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2407.12538 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2407.12538 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2407.12538 |
| locations[1].id | doi:10.48550/arxiv.2407.12538 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2407.12538 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5101809862 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8161-6787 |
| authorships[0].author.display_name | Juan Song |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Song, Juan |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101381210 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Jiaxiang He |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | He, Jiaxiang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5022848316 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0384-3743 |
| authorships[2].author.display_name | Mingtao Feng |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Feng, Mingtao |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5013877857 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6116-9286 |
| authorships[3].author.display_name | Keyan Wang |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Wang, Keyan |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5067798266 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0640-4060 |
| authorships[4].author.display_name | Yunsong Li |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Li, Yunsong |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5089986388 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5206-3842 |
| authorships[5].author.display_name | Ajmal Mian |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Mian, Ajmal |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2407.12538 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | High Frequency Matters: Uncertainty Guided Image Compression with Wavelet Diffusion |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10688 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9532999992370605 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Image and Signal Denoising Methods |
| related_works | https://openalex.org/W2382174632, https://openalex.org/W2129959498, https://openalex.org/W2784060934, https://openalex.org/W2902714807, https://openalex.org/W2537489131, https://openalex.org/W2046633342, https://openalex.org/W2394084632, https://openalex.org/W2358293514, https://openalex.org/W2059273319, https://openalex.org/W2014650515 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2407.12538 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2407.12538 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2407.12538 |
| primary_location.id | pmh:oai:arXiv.org:2407.12538 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2407.12538 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2407.12538 |
| publication_date | 2024-07-17 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 65, 76, 131 |
| abstract_inverted_index.To | 26 |
| abstract_inverted_index.We | 63 |
| abstract_inverted_index.an | 32, 121 |
| abstract_inverted_index.by | 75, 103 |
| abstract_inverted_index.in | 8, 22, 100, 155 |
| abstract_inverted_index.is | 167 |
| abstract_inverted_index.of | 114, 148 |
| abstract_inverted_index.on | 45, 141 |
| abstract_inverted_index.to | 85 |
| abstract_inverted_index.we | 30, 118 |
| abstract_inverted_index.Our | 42, 165 |
| abstract_inverted_index.R-D | 156 |
| abstract_inverted_index.and | 17, 81, 137, 162 |
| abstract_inverted_index.are | 57 |
| abstract_inverted_index.at: | 169 |
| abstract_inverted_index.for | 59, 70, 127 |
| abstract_inverted_index.low | 18, 96 |
| abstract_inverted_index.the | 50, 86, 95, 108, 111, 115, 146 |
| abstract_inverted_index.two | 142 |
| abstract_inverted_index.via | 49 |
| abstract_inverted_index.This | 88 |
| abstract_inverted_index.code | 166 |
| abstract_inverted_index.from | 110 |
| abstract_inverted_index.have | 3 |
| abstract_inverted_index.high | 14, 46, 54, 71 |
| abstract_inverted_index.loss | 125 |
| abstract_inverted_index.more | 132 |
| abstract_inverted_index.rate | 136 |
| abstract_inverted_index.that | 79 |
| abstract_inverted_index.this | 28 |
| abstract_inverted_index.with | 38 |
| abstract_inverted_index.(R-D) | 124 |
| abstract_inverted_index.codec | 78 |
| abstract_inverted_index.image | 23, 35, 61, 152 |
| abstract_inverted_index.issue | 98 |
| abstract_inverted_index.model | 69 |
| abstract_inverted_index.since | 53 |
| abstract_inverted_index.time. | 164 |
| abstract_inverted_index.common | 99 |
| abstract_inverted_index.design | 120 |
| abstract_inverted_index.direct | 101 |
| abstract_inverted_index.issue, | 29 |
| abstract_inverted_index.model, | 117 |
| abstract_inverted_index.models | 2 |
| abstract_inverted_index.random | 112 |
| abstract_inverted_index.UGDiff, | 149 |
| abstract_inverted_index.address | 27 |
| abstract_inverted_index.between | 135 |
| abstract_inverted_index.crucial | 58 |
| abstract_inverted_index.focuses | 44 |
| abstract_inverted_index.further | 119 |
| abstract_inverted_index.images. | 11 |
| abstract_inverted_index.methods | 154 |
| abstract_inverted_index.models. | 106 |
| abstract_inverted_index.propose | 31 |
| abstract_inverted_index.quality | 16 |
| abstract_inverted_index.remains | 20 |
| abstract_inverted_index.success | 7 |
| abstract_inverted_index.wavelet | 39, 51, 66 |
| abstract_inverted_index.However, | 12 |
| abstract_inverted_index.achieved | 5 |
| abstract_inverted_index.approach | 37, 43 |
| abstract_inverted_index.datasets | 144 |
| abstract_inverted_index.decoder. | 87 |
| abstract_inverted_index.details. | 62 |
| abstract_inverted_index.existing | 104 |
| abstract_inverted_index.fidelity | 97 |
| abstract_inverted_index.followed | 74 |
| abstract_inverted_index.paradigm | 92 |
| abstract_inverted_index.quality, | 159, 161 |
| abstract_inverted_index.rational | 133 |
| abstract_inverted_index.recently | 4 |
| abstract_inverted_index.residual | 77, 128 |
| abstract_inverted_index.sampling | 113 |
| abstract_inverted_index.tailored | 126 |
| abstract_inverted_index.validate | 145 |
| abstract_inverted_index.(UGDiff). | 41 |
| abstract_inverted_index.Diffusion | 0, 40 |
| abstract_inverted_index.addresses | 94 |
| abstract_inverted_index.available | 168 |
| abstract_inverted_index.balancing | 13 |
| abstract_inverted_index.benchmark | 143 |
| abstract_inverted_index.diffusion | 68, 89, 105, 116 |
| abstract_inverted_index.efficient | 33 |
| abstract_inverted_index.frequency | 47, 55, 72 |
| abstract_inverted_index.inference | 163 |
| abstract_inverted_index.introduce | 64 |
| abstract_inverted_index.providing | 130 |
| abstract_inverted_index.residuals | 84 |
| abstract_inverted_index.trade-off | 134 |
| abstract_inverted_index.transmits | 82 |
| abstract_inverted_index.components | 56 |
| abstract_inverted_index.compresses | 80 |
| abstract_inverted_index.distortion | 19 |
| abstract_inverted_index.generating | 9 |
| abstract_inverted_index.perceptual | 15, 158 |
| abstract_inverted_index.prediction | 83 |
| abstract_inverted_index.remarkable | 6 |
| abstract_inverted_index.subjective | 160 |
| abstract_inverted_index.surpassing | 150 |
| abstract_inverted_index.transform, | 52 |
| abstract_inverted_index.Considering | 107 |
| abstract_inverted_index.challenging | 21 |
| abstract_inverted_index.compression | 24, 36, 48, 91, 153 |
| abstract_inverted_index.conditional | 67 |
| abstract_inverted_index.distortion. | 138 |
| abstract_inverted_index.effectively | 93 |
| abstract_inverted_index.experiments | 140 |
| abstract_inverted_index.prediction, | 73 |
| abstract_inverted_index.uncertainty | 109 |
| abstract_inverted_index.compression, | 129 |
| abstract_inverted_index.high-quality | 10 |
| abstract_inverted_index.performance, | 157 |
| abstract_inverted_index.Comprehensive | 139 |
| abstract_inverted_index.applications. | 25 |
| abstract_inverted_index.effectiveness | 147 |
| abstract_inverted_index.probabilistic | 1 |
| abstract_inverted_index.reconstructing | 60 |
| abstract_inverted_index.rate-distortion | 123 |
| abstract_inverted_index.reconstructions | 102 |
| abstract_inverted_index.state-of-the-art | 151 |
| abstract_inverted_index.Uncertainty-Guided | 34 |
| abstract_inverted_index.uncertainty-weighted | 122 |
| abstract_inverted_index.prediction-then-residual | 90 |
| abstract_inverted_index.https://github.com/hejiaxiang1/Wavelet-Diffusion/tree/main | 170 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |