Deep Controllable Backlight Dimming

TL;DR

This paper introduces a deep learning-based local dimming algorithm for dual-panel HDR displays that predicts backlight values to enhance image quality while allowing user-controlled power-quality trade-offs.

Contribution

A novel CNN-based local dimming method that incorporates a controllable power parameter for improved HDR display quality and power efficiency.

Findings

Outperforms six other methods in display quality metrics.

Achieves better power consumption compared to existing approaches.

Provides user-controlled trade-off between power and image quality.

Abstract

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.