Abstract | ||
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High dynamic range (HDR) displays with dual-panels are one type of displays that can provide HDR content. These are composed of a white backlight panel and a colour LCD panel. Local dimming algorithms are used to control the backlight panel in order to reproduce content with high dynamic range and contrast at a high fidelity. However, existing local dimming algorithms usually process low dynamic range (LDR) images, which are not suitable for processing HDR images. In addition, these methods use hand-crafted features to estimate the backlight values, which may not be suitable for many kind of images. In this work, a novel deep learning based local dimming method is proposed for rendering HDR images on dual-panel HDR displays. The method uses a Convolutional Neural Network (CNN) to directly predict backlight values, using as input the HDR image that is to be displayed. The model is designed and trained via a controllable power parameter that allows a user to trade off between power and quality. The proposed method is evaluated against seven other methods on a test set of 105 HDR images, using a variety of quantitative quality metrics. Results demonstrate improved display quality and better power consumption when using the proposed method compared to the best alternatives. |
Year | DOI | Venue |
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2022 | 10.1109/TCE.2022.3188806 | IEEE Transactions on Consumer Electronics |
Keywords | DocType | Volume |
High dynamic range,local dimming,displays | Journal | 68 |
Issue | ISSN | Citations |
3 | 0098-3063 | 0 |
PageRank | References | Authors |
0.34 | 14 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Lvyin Duan | 1 | 0 | 0.34 |
Demetris Marnerides | 2 | 18 | 2.47 |
Alan Chalmers | 3 | 1113 | 96.80 |
Zhichun Lei | 4 | 0 | 1.35 |
Kurt Debattista | 5 | 548 | 60.32 |