Pixel Rarity Score : Rarity Learned Directly From Pixel Data
Conventional rarity scores rely on the meta-data of an NFT collection (the traits) to determine relative rarity within a collection. Such data is provided by the developers of the collection. In this paper, we discuss pixel-based rarity, which utilizes on raw pixel data to determine rarity. More specifically, we apply an algorithm that captures the pixels in the NFTs and assigns a rarity score to each pixel. It weighs the scores across all pixels within a set of NFTs to give an overall rarity score. We argue that by extracting the pixel data directly from the picture we could generate a richer and more systematic rarity computation. We compare rarity scores to pixel-based rarity scores and discuss their respective merits. Our results show that both scores, on average, show similar performance in predicting relative prices. A surprising result is that rarity and pixel rarity are weakly correlated. We argue that pixel rarity could be a viable metric in the case that the trait data does not properly reflect the visual features of the NFTs. Additionally, pixel-based rarity can be used to compare NFTs across multiple collections and could be a viable substitute for rarity scores where there is limited or no feature data available to compute conventional rarity scores
Year of publication: |
2023
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Authors: | Lommers, Kristof ; Storcheus, Dmitry ; Elsaadany, Abdelmoez ; Kancherla, Adi ; Baioumy, Mohamed |
Publisher: |
[S.l.] : SSRN |
Saved in:
freely available
Extent: | 1 Online-Ressource (5 p) |
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Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 6, 2023 erstellt |
Other identifiers: | 10.2139/ssrn.4350207 [DOI] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014257724
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