Photography — Archiving systems — Part 1: Best practices for digital image capture of cultural heritage material
ISO/TR 19263-1:2017 specifies how to perform quality analysis of imaging systems (e.g. flatbed scanners, planetary scanners, or digital still cameras) used for digitization of reflective two-dimensional originals. Original materials include but are not limited to books, textual documents, drawings, prints, photographs, and paintings. Certain types of two-dimensional materials with complex surface geometry and or highly reflective surface elements require special illumination techniques that can fall outside the scope of this document. NOTE ISO/TS 19264‑2 will address transmissive materials.
Photographie — Systèmes d'archivage — Partie 1: Meilleures pratiques pour la capture d'images numériques du matériel de patrimoine culturel
Standards Content (Sample)
Photography — Archiving systems —
Best practices for digital image
capture of cultural heritage material
Photographie — Systèmes d’archivage —
Partie 1: Meilleures pratiques pour la capture d’images numériques
du matériel de patrimoine culturel
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1 Scope . 1
2 Analysis of image quality . 1
2.1 General . 1
2.2 Image quality characteristics. 1
2.3 ISO 19264 Test chart technical features . 2
2.4 Grid and gray/white features . 2
2.4.1 General. 2
2.4.2 Running scale features (cm and inches) . 3
2.4.3 Grayscale and running gray/white/black bar features . 3
2.4.4 Colour patch features . 3
2.4.5 MTF measurement features . 4
2.4.6 Additional ISO 19264 target features/reference data . 4
2.5 Additional targets . 4
2.6 Linear grayscale . 5
2.6.1 DCSG colour chart . 5
2.6.2 Limitations of Chart Based Imaging System Analysis . 5
3 Image quality levels . 6
4 Basic principles of image capture and processing . 6
4.1 Overview . 6
4.2 Scene referred and output referred image states . 7
4.3 User controls and readouts . 7
4.3.1 General. 7
4.3.2 Colour Processing Controls . 7
4.3.3 Exposure readouts . 8
4.3.4 Raw processor readouts and controls . 8
4.3.5 Other user controls . 8
4.3.6 Unwanted data modification . 8
4.4 Master images and derivatives . 8
4.4.1 General. 8
4.4.2 Raw image files. 8
4.4.3 Artwork reproduction cycle . 9
5 Imaging system setup and calibration .10
5.1 General .10
5.2 Position camera system .10
5.3 Establish uniformity-even illumination .10
5.3.2 Optional flat-fielding .10
5.4 Establish exposure .11
5.5 Establish tone reproduction curve (OECF) .11
5.6 Create an ICC colour profile .11
5.7 Analyse colour and tone .12
6 Application of image quality analysis .12
6.1 Selection of imaging systems: preflighting equipment or vendors .12
6.2 Using ISO 19264 target: Initial system configuration .13
6.3 Using ISO 19264 target: System performance evaluation (benchmarking) .13
6.4 Using ISO 19264 target: Ongoing performance monitoring .13
7 Technical metadata for image quality analysis .14
Annex A (informative) Linear Grayscale L* to RGB conversion table .15
Annex B (informative) Subjective interpretive imaging (aesthetics) .16
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Today digitization programs need to satisfy the demands of an interconnected dynamic user
community. A digitized image can be repurposed across any number of systems and therefore needs to
be well defined, technically robust and media agnostic. The digital image of an original is intended to
satisfy multiple uses including access, archiving, research, conservation, education, marketing, social
media, reproduction and distribution both in print and online.
Intended for organizations, such as cultural heritage institutions, ISO 19264-1 specifies a method for
analysing imaging systems where it is important to control the degree of accuracy and to ensure that
imaging quality is maintained over time. There are three common applications of ISO 19264-1:
a) imaging system performance evaluation (benchmarking) – used for system development and
b) imaging system performance optimization – used for tailoring the system to a particular job
c) imaging system performance monitoring – used for controlling that the quality of the system
remains consistent and within specifications over time
The purpose of this document is to provide practical guidance on how to apply ISO 19264-1 for
cultural heritage imaging of two-dimensional originals. This includes how the image quality analysis
is performed, the function of technical target features, and how to adjust/optimize the performance
of imaging systems. Additionally this document illustrates how ISO 19264-1 can be used for selection
of appropriate imaging systems and how to establish and maintain image quality in digitization
Annex B provides information related to developing a digitization strategy including assessment of
collections, developing a hardware strategy and system selection.
ISO 19262 provides definitions for imaging terminology used in this document and ISO 19264-1.
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TECHNICAL REPORT ISO/TR 19263-1:2017(E)
Photography — Archiving systems —
Best practices for digital image capture of cultural heritage
This document specifies how to perform quality analysis of imaging systems (e.g. flatbed scanners,
planetary scanners, or digital still cameras) used for digitization of reflective two-dimensional
Original materials include but are not limited to books, textual documents, drawings, prints,
photographs, and paintings. Certain types of two-dimensional materials with complex surface
geometry and or highly reflective surface elements require special illumination techniques that can fall
outside the scope of this document.
NOTE ISO/TS 19264-2 will address transmissive materials.
2 Analysis of image quality
In order to analyse imaging system quality ISO 19264-1 specifies a technical target (ISO 19264-1 target)
designed to incorporate multiple technical features for the measurement of key imaging characteristics
from a single image. Calculations are performed via software dedicated to ISO 19264-1 target analysis.
2.2 Image quality characteristics
Image technical analysis involves a number of interrelated measurement steps, typically the analysis
process begins with validating white balance and tone reproduction followed by additional calculation
steps as listed below. When all measurements are within a set of defined tolerances, an imaging system
meets a defined quality level. Resolution and geometry are analysed after first analysing core image
— White Balance: adjustment of electronic still picture colour channel gains or image processing
so that radiation with relative spectral power distribution equal to that of the scene illumination
source is rendered as a visual neutral.
— Tone Reproduction Curve (TRC): curve graphically describing the relationship between the input
tones and the output tones in an imaging process.
— Gain Modulation (highlights/other patches): variation of the gain over the signal level.
— Noise: unwanted variations in the response of an imaging system.
— Dynamic Range: the difference, over a given period of time, between maximum and minimum
signal levels, expressed in decibels, contrast ratios or f-stops.
— Banding: unwanted stripes or bands that occur in a digital image.
— Defect Pixels: pixel or subpixel that operates in a way other than the one in which it is driven.
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— Colour Accuracy: ability of an imaging system to reproduce the colours of some intended object, as
specified using some colour difference metric.
— Sampling Rate (difference between claimed and obtained): number of samples per unit of time,
angle, revolutions or other mechanical, independent variable for uniformly sampled data.
— Resolution (limiting): measure of the ability of a camera system, or a component of a camera
system, to depict picture detail.
— Sharpening: amplification of the SFR by means of image processing to achieve sharper appearing
images. Also, a class of image processing operations that enhances the contrast of selective spatial
frequencies, usually visually important ones.
— MTF 50: the modulation transfer function is, a measure of the transfer of modulation (or contrast)
from the subject to the image and is used to measure spatial frequency response (SFR). In other
words, it measures how faithfully the imaging system reproduces (or transfers) detail from the
target to the digital image. MTF50 refers to that spatial frequency (expressed in lines per mm) at
which the image retains 50 % of the test target’s contrast, see ISO 12233.
— Illumination non-uniformity (target size related): application of visible radiation (light) to an object.
— Colour mis-registration: colour-to-colour spatial dislocation of otherwise spatially coincident
colour features of an imaged object.
— Distortion: displacement from the ideal shape of a subject (lying on a plane parallel to the image
plane) in the recorded image.
— Reproduction scale: ratio of the size of an object in a digital image and the size of the original object.
2.3 ISO 19264 Test chart technical features
The ISO 19264-1 target is defined in ISO 19264-1:—, Annex A. Individual chart features are reproduced
here to illustrate functionality. An ISO compliant target should contain all of the technical features.
Additional targets are utilized for characterizing imaging system colour and tone.
2.4 Grid and gray/white features
Figure 1 — Example of grid and gray/white features
Gray/white grids are used for analysing illumination non-uniformity and distortion. Illumination non-
uniformity is similar to white balance, but applies to illumination at all tonal levels across the entire
imaging field and can be adversely affected by the introduction of non-image forming light and or lens
falloff. Distortion is often corrected digitally, but doing so recalculates each pixel location in an image,
this may negatively influence image resolution but may also contribute to an overall improvement in
image reproduction accuracy. Illumination-non uniformity results are expressed as ΔL* differences
between the maximum and minimum L* values.
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2.4.2 Running scale features (cm and inches)
Figure 2 — Example of running scale
Scales are used to determine X and Y resolution, and to test for constant movement (scanners, stitching
NOTE This measured function identifies the actual imaged values in both x and y directions, assuring scale
integrity of the images
2.4.3 Grayscale and running gray/white/black bar features
Figure 3 — Example of grayscale and running gray/white/black bar features
The grayscale and running gray/white bars are used to determine OECF (tone recording), gain
modulation, noise, and signal to noise ratio
Imaging systems should convert the tone values in the original scene to digital values; this technical
term is OECF (Opto-Electronic Conversion Function). Validation of the correct selection of these
parameters and appropriate representation of the digital information for the selected parameters is a
critical function of image quality analysis.
Gain modulation refers to the variation of the gain (distribution of tonal values) over the signal level
and is a critical factor in reproduction imaging and colour accuracy. Reported as ΔL*values. The smaller
the deviation between the L* of the patches in the reference target and the L* values represented by the
digital code values the more accurate the tone reproduction.
Noise is generally the digital equivalent of film grain, and presents itself as pixel-to-pixel fluctuations
often seen in deep shadow areas. Noise has the effect of reducing the overall perceived smooth tonality
of an image. Noise can also take a one-dimensional form called banding or streaking.
Signal to noise ratio is the ratio of the incremental output signal to the root mean square (rms) noise
level, at a particular signal level.
2.4.4 Colour patch features
Figure 4 — Example of colour patch features
The colour patch element is used for determination of colour accuracy, test of the colour space, validation
of ICC profiles, and survey of colour variation across the scanning area.
Results are reported in ΔE 2000* values in the form of a table for each individual patch together with
the result for the mean and the max value for all patches. It is sufficient to report the mean and the
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max value only. Observation of the best 90 % can be helpful to help identify outlying data but is not
ΔE 2000* values are calculated using a linear (SL=1) formula (see ISO 19264-1).
2.4.5 MTF measurement features
Figure 5 — Example of MTF measurement features
The MTF element enables measurement of sampling resolution according to ISO 16067-1 (up to
1200 PPI max.).
Resolution (Limiting) is the highest frequency (spacing) that image detail can be distinguished.
Scanners and cameras may claim very high resolutions that are unachievable due to design limitations
of the total imaging system. This measure identifies the actual achieved resolution and should not be
confused or considered equivalent to sampling rate.
This chart element also helps calculate sampling efficiency, and provides for visual resolution check
up to 18 lp/mm. Sampling efficiency is also calculated using the MTF. Example-if the object captured
is 10 in long and the sensor has 4000 pixel features capturing the 10 inches, the sampling rate is
400 pixels/in. Most imaging systems cannot achieve 100 % sampling efficiency. An accurate sampling
rate is essential to knowing the size of the original object.
2.4.6 Additional ISO 19264 target features/reference data
Additional chart areas may be designated for labelling, additional test patterns or chart features and
manufacturing information. Chart Reference Data are typically custom measured and delivered from
test chart vendors in text table form to be used as a reference for calculations. Chart reference data sets
and measurement methods should be documented.
2.5 Additional targets
In addition to the ISO 19264-1 target other targets may be used to characterize the imaging system.
The following targets aid in the characterization of imaging systems.
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2.6 Linear grayscale
1 semi gloss values (5L* to 95*L)
2 gloss black (4L*)
3 measurement scale, in mm
4 perceptual middle value (50L*)
Figure 6 — Example of linear grayscale
A linear grayscale is useful for configuration and verification of tone reproduction (OECF) and gain
modulation. The target incorporates semi-gloss spectrally neutral pigments equally spaced in 5L* steps
from L*5 to L*95 with additional gloss black patches. The gloss patches extend the dynamic range and
are used to visually assess lighting reflections and glare from improper lighting geometry.
2.6.1 DCSG colour chart
Figure 7 — Example of DCSG colour chart
The X Rite Colour Checker Digital SG (DCSG) colour chart is useful for colour calibration (device
characterization). Colour charts may vary in terms of substrate, gloss factor, colour gamut and number
of patches. A colour chart that closely matches the surface quality and colour gamut of the original
artwork may be utilized.
2.6.2 Limitations of Chart Based Imaging System Analysis
Being that ISO 19264 is based upon analysis of test charts with technical patterns and reference values
there are inherent limitations that need to be considered. Fabrication of technical targets varies over
time, and targets have a finite life span. Baseline data used to define technical targets (chart reference
data) can also vary between users and vendors. Vendors may improperly implement the analysis
methods outlined in ISO 19264. Beyond these possible variables, there are variables in the surface
qualities of original artworks, capture illuminants and sensors that limit the ability to ensure an exact
colourimetric or perceptual match.
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3 Image quality levels
Image analysis of a technical target results in an array of values. A core element of 19264-1 is the use
of aims and tolerances to provide valuable insight into image quality. These aims and tolerances have
been derived via extensive testing and feedback from cultural heritage imaging users and program
ISO 19264-1 defines three image quality levels presented as a matrix. It is important to note that these
quality levels are not provided for any specific use case or category of artwork therefore reaching the
highest imaging quality threshold for all categories is not a universal requirement. The quality levels are
meant to provide users with a reference to gauge relative image quality and to help establish workflow
baselines. End users, user communities, or institutions may refer to the 19264-1 quality level matrix
as needed to address different object types, to document and share results or to specify image quality
requirements as part of contractual agreements with outside digitization vendors. Program managers
may choose to configure and maintain systems that exceed the tolerance definition matrix defined in
ISO 19264-1. It is important to document any site or project specific quality aims.
Please refer to the image quality table in ISO 19264-1.
4 Basic principles of image capture and processing
In order to record an original digital imaging systems generally follow the steps outlined in the flow
diagram shown in Figure 9 which illustrates a typical array sensor device.
Figure 8 — Typical array sensor device
The reflected, or transmitted light from the object is collected by the optics and detected by an image
sensor. The detected data may then be processed for sensor defects and exposure uniformity. If the
imaging system used a colour filter array (CFA), the result is an encoded data array corresponding to a
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spatial pattern of repeated, e.g. red, green and blue, signals. At this point these raw data constitute the
first form of ‘raw’ recorded image, the raw corrected CFA data.
The next step in a typical processing path is the generation of a fully populated three-colour image
array. Propriety algorithms, aimed at minimizing colour artefacts, can be applied here. This de-
mosaicing operation is the interpolation of the single-record array to a ‘raw’ interpolated red, green
and blue data set. While de-mosaicing algorithms have improved over time, reproduction of certain
originals with halftones, etchings and other materials with high frequency visual patterns can suffer
from colour Moire artefacts. Moire is defined as a spatial beat phenomenon generated by the modulation
of numerous spatial frequencies. Moire artefacts can impact both luminance and chrominance. Line
scanners, and multi-shot sensor systems minimize the occurrence of colour Moire artefacts as de-
mosaicing is not necessary in these imaging systems.
White-balance, and matrix colour-correction operations are usually applied next. The result is an image
data set that is in a scene-referred colour encoding.
The final step in the image processing chain is the rendering, usually for display. The result is a finished
image data array in an output-referred colour encoding. This step may be a simple colour-space
transformation, but can also include choices for gamut mapping and colour preference.
While the above steps are common in colour image acquisition systems, specific implementation details
will vary. Understanding the signal (colour)