2 research outputs found
Deep Controllable Backlight Dimming
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
A note on synthesizing geodesic based contact curves
The paper focuses on synthesizing optimal contact curves that can be used to
ensure a rolling constraint between two bodies in relative motion. We show that
geodesic based contact curves generated on both the contacting surfaces are
sufficient conditions to ensure rolling. The differential geodesic equations,
when modified, can ensure proper disturbance rejection in case the system of
interacting bodies is perturbed from the desired curve. A corollary states that
geodesic curves are generated on the surface if rolling constraints are
satisfied. Simulations in the context of in-hand manipulations of the objects
are used as examples