Don Williams

Low-frequency MTF estimation for digital imaging devices using slanted-edge analysis

Modulation transfer function (MTF) metrology and interpretation for digital image capture devices has usually concentrated on mid- to high-frequency information, relative to the half-sampling frequency. These regions typically quantify characteristics and operations such as sharpening, limiting resolution, and aliasing. However, a potential wealth of low-frequency, visually significant information is often masked in existing measurement results because of spatial data truncation. For print or document scanners, this influences measurements in the spatial frequency range of 0 to 2.0 cycles/mm, where the effects of veiling flare, micro flare, and integrating cavity effect (ICE) often manifest themselves. Using a form of edge-gradient analysis based on slanted edges, we present a method for measurement of these characteristics. By carefully adapting this well-established technique, these phenomena can be quantified. We also show how, in many cases, these effects can be treated as other spread-function or device-MTF components. The theory and field metrology of several devices using the adapted technique are also presented.

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.

Identification of image noise sources in digital scanner evaluation

For digital image acquisition systems, analysis of image noise often focuses on random sources, such as those associated with quantum signal detection and signal-independent fluctuations. Other important noise sources result in pixel-to-pixel sensitivity variations that introduce repeatable patterns into the image data. In addition, since most analyses use a nominally uniform target area to estimate image noise statistics, target noise can often masquerade as noise introduced by the device under test. We described a method for distilling various fixed-pattern and temporal noise sources. The method uses several replicate digital images, acquired in register. In some cases, however, evaluation of digital scanners reveals, scan-to-scan variation in the image registration to the input test target. To overcome this limitation, a modified noise estimation method is described. This includes a step to correct this scan-to-scan misregistration. We also show how the measurement of temporal and fixed pattern noise sources can be achieved via the noise color covariance from a single test image.

A pilot study of digital camera resolution metrology protocols proposed under ISO 12233, edition 2

Edition 2 of ISO 12233, Resolution and Spatial Frequency Response (SFR) for Electronic Still Picture Imaging, is likely to offer a choice of techniques for determining spatial resolution for digital cameras different from the initial standard. These choices include 1) the existing slanted-edge gradient SFR protocols but with low contrast features, 2) polar coordinate sine wave SFR technique using a Siemens star element, and 3) visual resolution threshold criteria using a continuous linear spatial frequency bar pattern features. A comparison of these methods will be provided. To establish the level of consistency between the results of these methods, theoretical and laboratory experiments were performed by members of ISO TC42/WG18 committee. Test captures were performed on several consumer and SLR digital cameras using the on-board image processing pipelines. All captures were done in a single session using the same lighting conditions and camera operator. Generally, there was good conformance between methods albeit with some notable differences. Speculation on the reason for these differences and how this can be diagnostic in digital camera evaluation will be offered.

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.

Evolution of slanted edge gradient SFR measurement

The well-established Modulation Transfer Function (MTF) is an imaging performance parameter that is well suited to describing certain sources of detail loss, such as optical focus and motion blur. As performance standards have developed for digital imaging systems, the MTF concept has been adapted and applied as the spatial frequency response (SFR). The international standard for measuring digital camera resolution, ISO 12233, was adopted over a decade ago. Since then the slanted edge-gradient analysis method on which it was based has been improved and applied beyond digital camera evaluation. Practitioners have modified minor elements of the standard method to suit specific system characteristics, unique measurement needs, or computational shortcomings in the original method. Some of these adaptations have been documented and benchmarked, but a number have not. In this paper we describe several of these modifications, and how they have improved the reliability of the resulting system evaluations. We also review several ways the method has been adapted and applied beyond camera resolution.

Adapting the ISO 20462 softcopy ruler method for online image quality studies

In this paper we address the problem of Image Quality Assessment of no reference metrics, focusing on JPEG corrupted images. In general no reference metrics are not able to measure with the same performance the distortions within their possible range and with respect to different image contents. The crosstalk between content and distortion signals influences the human perception. We here propose two strategies to improve the correlation between subjective and objective quality data. The first strategy is based on grouping the images according to their spatial complexity. The second one is based on a frequency analysis. Both the strategies are tested on two databases available in the literature. The results show an improvement in the correlations between no reference metrics and psycho-visual data, evaluated in terms of the Pearson Correlation Coefficient.

INCITS W1.1 development update: appearance-based image quality standards for printers

In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard.(1),(2) The resulting W1.1 project is based on a proposal(3) that perceived image quality can be described by a small set of broad-based attributes. There are currently six ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress of the teams addressing the attributes of Macro-Uniformity, Colour Rendition, Gloss & Gloss Uniformity, Text & Line Quality and Effective Resolution.

Applying and extending ISO/TC42 digital camera resolution standards to mobile imaging products

There are no fundamental differences between todays mobile telephone cameras and consumer digital still cameras that suggest many existing ISO imaging performance standards do not apply. To the extent that they have lenses, color filter arrays, detectors, apertures, image processing, and are hand held, there really are no operational or architectural differences. Despite this, there are currently differences in the levels of imaging performance. These are driven by physical and economic constraints, and image-capture conditions. Several ISO standards for resolution, well established for digital consumer digital cameras, require care when applied to the current generation of cell phone cameras. In particular, accommodation of optical flare, shading non-uniformity and distortion are recommended. We offer proposals for the application of existing ISO imaging resolution performance standards to mobile imaging products, and suggestions for extending performance standards to the characteristic behavior of camera phones.

Measurement of texture loss for JPEG 2000 compression

The capture and retention of image detail are important characteristics for system design and subsystem selection. An established imaging performance measure that is well suited to certain sources of detail loss, such as optical focus and motion blur, is the Modulation Transfer Function (MTF). Recently we have seen the development of image quality methods aimed at more adaptive operations, such as noise cleaning and adaptive digital filtering. An example of this is the measure of texture (image detail) loss using sets of overlapping small objects, known as dead leaves targets. In this paper we investigate the application of the above method to image compression. We apply several levels of JPEG and JPEG 2000 compression to digital images that include scene content that is amenable to the texture loss measure. A modified form of the method was used. This allowed direct target compensation without data smoothing. Following a camera simulation, the texture MTF and acutance were computed. The standard deviation of the acutance measure was 0.014 (relative error of 1.63%), found by replicate measurements. Structured similarity index (SSIM) values, used for still and video image quality evaluation, were also computed for the image sets. The acutance and SSI results were similar; however the relationship between the two showed an offset between the JPEG and JPEG 2000 images sets.