Toward effective local dimming-driven liquid crystal displays: a deep curve estimation–based adaptive compensation solution.
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| Title: | Toward effective local dimming-driven liquid crystal displays: a deep curve estimation–based adaptive compensation solution. |
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| Authors: | Liu, Tianshan1 tianshan.liu@connect.polyu.hk, Lam, Kin-Man2 enkmlam@polyu.edu.hk |
| Source: | Journal of Electronic Imaging. Sep2024, Vol. 33 Issue 5, p53005-13. 1p. |
| Subjects: | Learning strategies, Vanilla, Industrial applications |
| Abstract: | Local backlight dimming (LBD) is a promising technique for improving the contrast ratio and saving power consumption for liquid crystal displays. LBD consists of two crucial parts, i.e., backlight luminance determination, which locally controls the luminance of each sub-block of the backlight unit (BLU), and pixel compensation, which compensates for the reduction of pixel intensity, to achieve pleasing visual quality. However, the limitations of the current deep learning–based pixel compensation methods come from two aspects. First, it is difficult for a vanilla image-to-image translation strategy to learn the mapping relations between the input image and the compensated image, especially without considering the dimming levels. Second, the extensive model parameters make these methods hard to be deployed in industrial applications. To address these issues, we reformulate pixel compensation as an input-specific curve estimation task. Specifically, a deep lightweight network, namely, the curve estimation network (CENet), takes both the original input image and the dimmed BLUs as input, to estimate a set of high-order curves. Then, these curves are applied iteratively to adjust the intensity of each pixel to obtain a compensated image. Given the determined BLUs, the proposed CENet can be trained in an end-to-end manner. This implies that our proposed method is compatible with any backlight dimming strategies. Extensive evaluation results on the DIVerse 2K (DIV2K) dataset highlight the superiority of the proposed CENet-integrated local dimming framework, in terms of model size and visual quality of displayed content. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 180820323 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Toward effective local dimming-driven liquid crystal displays: a deep curve estimation–based adaptive compensation solution. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Tianshan%22">Liu, Tianshan</searchLink><relatesTo>1</relatesTo><i> tianshan.liu@connect.polyu.hk</i><br /><searchLink fieldCode="AR" term="%22Lam%2C+Kin-Man%22">Lam, Kin-Man</searchLink><relatesTo>2</relatesTo><i> enkmlam@polyu.edu.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Electronic+Imaging%22">Journal of Electronic Imaging</searchLink>. Sep2024, Vol. 33 Issue 5, p53005-13. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Learning+strategies%22">Learning strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Vanilla%22">Vanilla</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+applications%22">Industrial applications</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Local backlight dimming (LBD) is a promising technique for improving the contrast ratio and saving power consumption for liquid crystal displays. LBD consists of two crucial parts, i.e., backlight luminance determination, which locally controls the luminance of each sub-block of the backlight unit (BLU), and pixel compensation, which compensates for the reduction of pixel intensity, to achieve pleasing visual quality. However, the limitations of the current deep learning–based pixel compensation methods come from two aspects. First, it is difficult for a vanilla image-to-image translation strategy to learn the mapping relations between the input image and the compensated image, especially without considering the dimming levels. Second, the extensive model parameters make these methods hard to be deployed in industrial applications. To address these issues, we reformulate pixel compensation as an input-specific curve estimation task. Specifically, a deep lightweight network, namely, the curve estimation network (CENet), takes both the original input image and the dimmed BLUs as input, to estimate a set of high-order curves. Then, these curves are applied iteratively to adjust the intensity of each pixel to obtain a compensated image. Given the determined BLUs, the proposed CENet can be trained in an end-to-end manner. This implies that our proposed method is compatible with any backlight dimming strategies. Extensive evaluation results on the DIVerse 2K (DIV2K) dataset highlight the superiority of the proposed CENet-integrated local dimming framework, in terms of model size and visual quality of displayed content. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Electronic Imaging is the property of SPIE - International Society of Optical Engineering and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1117/1.JEI.33.5.053005 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: 53005 Subjects: – SubjectFull: Learning strategies Type: general – SubjectFull: Vanilla Type: general – SubjectFull: Industrial applications Type: general Titles: – TitleFull: Toward effective local dimming-driven liquid crystal displays: a deep curve estimation–based adaptive compensation solution. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Tianshan – PersonEntity: Name: NameFull: Lam, Kin-Man IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10179909 Numbering: – Type: volume Value: 33 – Type: issue Value: 5 Titles: – TitleFull: Journal of Electronic Imaging Type: main |
| ResultId | 1 |
