Christopher L. DuMont

Assessing the Quality of Motion Picture Systems from Scene-to-Digital Data

Numerous factors contribute to onscreen image quality of cinema systems. Using a combination of new and traditional measurement techniques, the image quality of different capture technologies is measured; a framework for the analysis is defined; and metrics of limiting resolution, frequency response slope, image sharpness, sharpening artifacts, aliasing, and depth of field are reviewed. Highlighted is the dynamic range in terms of a Universal Model, which applies to digital intermediate (or data-centric) cinema imaging systems. This paper compares the values of these performance metrics for film and electronic-based imaging technologies- important because they influence how well a scene is represented to the viewer. We hope that this knowledge will improve onscreen image quality and provide cinematographers and directors with the tools to give cinema and in-home viewers a richer experience.

Design Improvements for Motion Picture Film Projectors

This paper describes improvements developed for motion picture film projectors, in particular, new designs for the intermittent, or Geneva mechanism, and for a Universal lamp house. These new designs are intended to improve the quality of the overall screen image as well as light efficiency and uniformity, resulting in significant increases in screen luminance.

Aliasing and Reconstruction Distortion in Digital Intermediates

This paper addresses two types of artifacts associated with the image sampling and reconstruction process, namely, aliasing and reconstruction distortion. Aliasing is an artifact that results from sampling a continuous signal at too low of a spatial rate relative to the input frequency content. Shannons sampling theorem states that discrete sampling of a signal at a uniform rate higher than twice the highest frequency in the signal, called the Nyquist rate, will allow a perfect reconstruction of the original continuous signal. However, image displays do not reconstruct images according to the ideal reconstruction equation, and, in many cases, the display uses nothing more than a sample-and-hold reconstruction. It has long been known that nonideal reconstruction can lead to distortion of the image data at frequencies below the Nyquist rate. Proper recognition of the distinction between aliasing and reconstruction errors can mean the difference between accepting and avoiding artifacts.

An introduction to aliasing and sharpening in digital motion picture systems

Many interacting factors affect image quality. This paper discusses the measurement of some crucial factors and reviews their interactions. Specifically, how pixel count can interact with the image-quality factors of limiting resolution, aliasing ratio, and shape of frequency response. A new tool designed for on-screen image-quality measurements for both film and electronic projectors is described, and actual examples of aliasing artifacts are shown in still images from digital motion picture systems. It is then illustrated how aliasing artifacts can arise, although Nyquist sampling requirements are satisfied. Finally, some of the interactions between limiting resolution and frequency response shape for still and moving images are explained.

Relationships between pixel count, aliasing, and limiting resolution in digital motion picture systems

This paper analyzes how pixel count affects one type of aliasing artifact and image rendition near limiting resolution. A previous paper 1 showed that aliasing artifacts take many forms. This paper focuses on the aliasing artifact identified in the ISO 12233 standard and identified as Type A aliasing in the prior paper. A relationship termed Type A aliasing equation is presented, which predicts aliasing as measured by the ISO 12233 standard. It is then demonstrated that this equation predicts the best unreconstructed aliasing performance for digital motion picture systems and subsystems, thereby defining one characteristic of an ideal system. The predicted result is then compared by the Type A aliasing equation and the measured aliasing performance of 20 different digital motion picture systems and subsystems. It is also shown that the Type A aliasing equation is superior to the classical Nyquist theory as a predictor of aliasing performance of digital motion picture systems. Finally, the equation is used to compute the minimum number of pixels required for a given aliasing level and limiting resolution, and data is presented to determine the pixel count required to render a given limiting resolution.