Peter D. Burns

Imaging performance taxonomy

A significant challenge in the adoption of todays digital imaging standards is a clear connection to how they relate to todays vernacular digital imaging vocabulary. Commonly used terms like resolution, dynamic range, delta E, white balance, exposure, or depth of focus are mistakenly considered measurements in their own right and are frequently depicted as a disconnected shopping list of individual metrics with little common foundation. In fact many of these are simple summary measures derived from more fundamental imaging science/engineering metrics, adopted in existing standard protocols. Four important underlying imaging performance metrics are; Spatial Frequency Response (SFR), Opto-Electronic Conversion Function (OECF), Noise Power Spectrum (NPS), and Spatial Distortion. We propose an imaging performance taxonomy. With a primary focus on image capture performance, our objective is to indicate connections between related imaging characteristics, and provides context for the array of commonly used terms. Starting with the concepts of Signal and Noise, the above imaging performance metrics are related to several simple measures that are compatible with testing for design verification, manufacturing quality assurance, and technology selection evaluation.

Sampling efficiency in digital camera performance standards

One of the first ISO digital camera standards to address image microstructure was ISO 12233, which introduced the SFR, spatial frequency response, based on the analysis of edge features in digital images. The SFR, whether derived from edges or periodic signals, describes the capture of image detail as a function of spatial frequency. Often during camera testing, however, there is an interest in distilling SFR results down to a single value that can be compared with acceptable tolerances. As a measure of limiting resolution, it has been suggested that the frequency at which the SFR falls to, e.g., 10%, can be used. We use this limiting resolution to introduce a sampling efficiency measure, being considered under the current ISO 12233 standard revision effort. The measure is the ratio of limiting resolution frequency to that implied by the image (sensor) sampling alone. The rationale and details of this measure are described, as are example measurements. One-dimensional sampling efficiency calculations for multiple directions are included in a two-dimensional analysis.

Estimation error in image quality measurements

The development and adoption of standard image quality measurement and analysis methods have helped both the evaluation of competing imaging products and in technologies. Inherent in the interpretation of results from any particular evaluation, however, are the variation of the method itself, the sampling of test images, equipment, and test conditions. Here we take a statistical approach to measurement variation, and interpret the objective as being the estimation of particular system or image properties, based on data, collected as part of standard testing. Measurement variation was investigated for two signal-transfer methods commonly used for digital camera and scanner evaluation: the ISO 12233 slanted-edge spatial frequency response and the dead-leaves method for texture-MTF evaluation being developed by the Camera Phone Image Quality (CPIQ) Initiative. In each case, the variation due to the selection of analysis regions was computed by repeated analysis. The slanted-edge methods indicated a relative error in the range of 1-3% depending on the nature of the region selection. For the dead-leaves method, the amplitude spectrum (square root of the noise-power spectrum) showed a relative error of approximately 4-6%, however, this can be reduced by applying spectral estimation methods commonly used in image noise analysis.

Comparing image quality of print-on-demand books and photobooks from web-based vendors

Because of the emergence of e-commerce and developments in print engines designed for economical output of very short runs, there are increased business opportunities and consumer options for print-on-demand books and photobooks. The current state of these printing modes allows for direct uploading of book files via the web, printing on nonoffset printers, and distributing by standard parcel or mail delivery services. The goal of this research is to assess the image quality of print-on-demand books and photobooks produced by various Web-based vendors and to identify correlations between psychophysical results and objective metrics. Six vendors were identified for one-off (single-copy) print-on-demand books, and seven vendors were identified for photobooks. Participants rank ordered overall quality of a subset of individual pages from each book, where the pages included text, photographs, or a combination of the two. Observers also reported overall quality ratings and price estimates for the bound books. Objective metrics of color gamut, color accuracy, accuracy of International Color Consortium profile usage, eye-weighted root mean square L * , and cascaded modulation transfer acutance were obtained and compared to the observer responses. We introduce some new methods for normalizing data as well as for strengthening the statistical significance of the results. Our approach includes the use of latent mixed-effect models. We found statistically significant correlation with overall image quality and some of the spatial metrics, but correlations between psychophysical results and other objective metrics were weak or nonexistent. Strong correlation was found between psychophysical results of overall quality assessment and estimated price associated with quality. The photobook set of vendors reached higher image-quality ratings than the set of print-on-demand vendors. However, the photobook set had higher image-quality variability.