Deep Controllable Backlight Dimming for HDR Displays

• Published in: IEEE transactions on consumer electronics Vol. 68; no. 3; pp. 191 - 199
• Main Authors: Duan, Lvyin, Marnerides, Demetris, Chalmers, Alan, Lei, Zhichun, Debattista, Kurt
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Backlights; Dimming; Displays; Dynamic range; Heuristic algorithms; High dynamic range; Image quality; Image resolution; Lighting; Liquid crystal displays; local dimming; Machine learning; Power consumption; Power demand
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2022.3188806

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.