One-stop measurement model for fast and accurate tensor display characterization

Abstract

Many light field displays are fundamentally different from other displays in that they do not have quantized pixels, quantized angular outputs, or a physical screen position, which can make definitions and characterization problematic. We have determined that it is more appropriate to express the spatial resolution in terms of spatial cutoff frequency rather than a physical distance as in the case of a display with actual quantized pixels. This concept is then extended to also encompass angular resolution. The technique exploits the fact that when spatial resolution of a sinusoidal grating pattern is halved, its contrast ratio is reduced by a known proportion. An improved model, based on an earlier design concept, has been developed. It not only can be used to measure spatial and angular cutoff frequencies, but also can enable comprehensive characterization of the display. This model provides fast, simple measurement with good accuracy. It does not use special equipment or require time-consuming subjective evaluations. Using the model to characterize images in a rapid, accurate manner validates the effectiveness of this technique.

Increasing depth of field for slanted lenticular 3D displays

“…The resulting image was photographed at the display's optimum viewing distance, which in this case was set at 350 mm from the screen. The image was analyzed on accordance with the procedure described in the paper [10].…”

“…In MV and SMV displays, the goal is to make the display show a larger depth range for objects, while also maintaining perceived resolution within an acceptable range. The ability of a glasses-free 3D display to show the depth of objects can be represented by the DOF [9,10]. DOF is defined as the distance between the front and back of the image volume where the perceived resolution is better than or equal to half the resolution at the screen [11,12].…”

Recognition Method for Face Local Features Based on Fuzzy Algorithm and Intelligent Data Analysis

“…e principal component analysis of the face is to find the most orthogonal basis of face features [38][39][40][41][42][43] so that the mean square error between the transformed variable and the original variable is the smallest. e so-called "principal component" is to transform the original independent variable into another group of variables, and then some important components are used as independent variables.…”