LoCoPalettes: Local Control for Palette‐based Image Editing
Palette‐based image editing takes advantage of the fact that color palettes are intuitive ions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in m...
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| Vydáno v: | Computer graphics forum Ročník 42; číslo 4 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Blackwell Publishing Ltd
01.07.2023
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Palette‐based image editing takes advantage of the fact that color palettes are intuitive ions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method. |
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| AbstractList | Palette‐based image editing takes advantage of the fact that color palettes are intuitive ions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method. Palette‐based image editing takes advantage of the fact that color palettes are intuitive abstractions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method. |
| Author | Echevarria, Jose Chao, Cheng‐Kang Ted Tan, Jianchao Gingold, Yotam Klein, Jason |
| Author_xml | – sequence: 1 givenname: Cheng‐Kang Ted orcidid: 0000-0001-6845-0890 surname: Chao fullname: Chao, Cheng‐Kang Ted organization: George Mason University – sequence: 2 givenname: Jason orcidid: 0009-0002-2015-7809 surname: Klein fullname: Klein, Jason organization: Cornell University – sequence: 3 givenname: Jianchao orcidid: 0000-0002-9862-2654 surname: Tan fullname: Tan, Jianchao organization: Kuaishou Technology – sequence: 4 givenname: Jose orcidid: 0000-0001-6802-0911 surname: Echevarria fullname: Echevarria, Jose organization: Adobe Research – sequence: 5 givenname: Yotam orcidid: 0000-0002-5381-2104 surname: Gingold fullname: Gingold, Yotam organization: George Mason University |
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| Copyright | 2023 The Authors. Computer Graphics Forum published by Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd. 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Algorithms CCS Concepts Color Computing methodologies → Image processing Editing Semantics |
| Title | LoCoPalettes: Local Control for Palette‐based Image Editing |
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