论文标题
深度可控背光灯调光
Deep Controllable Backlight Dimming
论文作者
论文摘要
双面板显示需要局部调光算法,以便以高保真和高动态范围重现内容。在这项工作中,提出了一种基于深度学习的新型局部调光方法,用于在双板HDR显示器上渲染HDR图像。该方法使用卷积神经网络来预测背光值,并使用将显示的HDR图像作为输入。该模型是通过可控的功率参数设计和训练的,该参数允许用户在功率和质量之间进行权衡。使用各种定量质量指标,使用105个HDR图像的测试集对其他六种方法评估了所提出的方法。结果表明,与最佳替代方案相比,使用提出的方法时,显示质量的提高和更好的功耗。
Dual-panel displays require local dimming algorithms in order to reproduce content with high fidelity and high dynamic range. 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 to 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 six 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.