深度可控背光调光
Deep Controllable Backlight Dimming
摘要：
双面板显示器需要局部调光算法，以便以高保真度和高动态范围再现内容。在这项工作中，提出了一种新的基于深度学习的局部调光方法，用于在双面板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，使用要显示的HDR图像作为输入。该模型通过可控的功率参数进行设计和训练，允许用户在功率和质量之间进行权衡。所提出的方法进行评估，对其他六种方法的测试集的105 HDR图像，使用各种定量质量指标。结果表明，改进的显示质量和更好的功耗时，使用所提出的方法相比，最好的替代品。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
