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NISTIR 7779

An Exploration of the Operational 

Ramifications of Lossless Compression of 

1000 ppi Fingerprint Imagery

Shahram Orandi

John M. Libert 

John D. Grantham

Kenneth Ko

Stephen S. Wood

Jin Chu Wu

Lindsay M. Petersen

Bruce Bandini

http://dx.doi.org/10.6028/NIST.IR.7779
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NISTIR 7779

An Exploration of the Operational 

Ramifications of Lossless Compression of 

1000 ppi Fingerprint Imagery

Shahram Orandi

John M. Libert

Kenneth Ko

Stephen S. Wood

Jin Chu Wu

Information Access Division - Image Group 

Information Technology Laboratories

John D. Grantham

Systems Plus, Inc.

Lindsay M. Petersen

MITRE Corporation

Bruce Bandini

Booz Allen Hamilton, Inc.

http://dx.doi.org/10.6028/NIST.IR.7779

August 2012

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
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ACKNOWLEDGEMENTS

The authors wish to give special thanks to the following individuals and organizations for their support of this work:

Federal Bureau of Investigation for all their support throughout this study

T.J. Smith and the LA County Sheriff’s Department 

R. Michael McCabe, IDTP

Margaret Lepley, MITRE

In addition, we are grateful for and appreciate the guidance, support and coordination provided by Michael D. Garris,

without whose help this study would not have been possible.

DISCLAIMER

Specific hardware and software products identified in this report were used in order to perform the evaluations 

described in this document. In no case does identification of any commercial product, trade name, or vendor, imply 

recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the 

products and equipment identified are necessarily the best available for the purpose.
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EXECUTIVE SUMMARY

The criminal justice communities throughout the world exchange fingerprint imagery data primarily in 8-bit gray-scale

and at 500 pixels per inch1(ppi) or equivalently 19.7 pixels per millimeter (ppmm). The Wavelet Scalar Quantization 

(WSQ) fingerprint image compression algorithm has been developed and maintained by the Federal Bureau of 

Investigation (FBI), Los Alamos National Laboratory and the National Institute for Standards and Technology (NIST) as 

the standard for compressing 500 ppi fingerprint imagery in the United States. WSQ is classified as a “lossy” 

compression algorithm. Lossy compression algorithms employ data encoding methods which discard (lose) some of the 

data in the encoding process in order to achieve an aggressive reduction in the size of the data being compressed. 

Decompressing the resulting compressed data yields content that, while different from the original, is similar enough to 

the original that it remains useful for the intended purpose. The WSQ algorithm allows users to specify how much 

compression is to be applied to the fingerprint image at the cost of increasingly greater loss in fingerprint image fidelity 

as the effective compression ratio is increased (see Figure 1 for an example of image degradation from lossy 

compression). 

The importance of latent images (images lifted from the crime scene, via uncontrolled collection) in criminal casework

has been growing. Anecdotal evidence from latent fingerprint examiners has indicated that any fidelity loss as a result 

of compression should be avoided prior to review of those fingerprints by examiners and that even small increases in 

the amount of higher level detail may yield benefits. Because of this, most latent fingerprint images used in casework 

are either transferred in non-compressed form or compressed using a lossless algorithm where the resulting 

compressed representation of the image can be decompressed to yield an image exactly identical to the original.

While extensive effort has been put into standards and certification pathways for lossy fingerprint compression, 

lossless compression strategies have not been thoroughly examined.

This study examines various lossless compression algorithms and the relative advantages and disadvantages of each 

with respect to effective compression rates, compression throughput and decompression throughput. This study also 

examines different implementations of the same algorithm, as well as implementations of the same algorithm 

generated for different computer system architectures (e.g., 32-bit & 64-bit).

This study finds that wavelet-based compression algorithms such as JPEG 20002generally yield better effective 

compression rates than the non-wavelet-based algorithms such as PNG (Portable Network Graphics). However, nonwavelet-based algorithms tend to have higher throughput (require less time to operate on data) than wavelet-based 

algorithms. This study also shows that while architectural differences do not yield much operational difference in terms 

of effective compression rates, such architectural differences do translate to significant differences in compression and 

decompression throughputs.

 

1Resolution values for fingerprint imagery are specified in pixels per inch (ppi) throughout this document. This is based on widely 

used specification guidelines for such imagery and is accepted as common nomenclature within the industry. SI units for these will be 

presented only once.

2

The “2000” refers to the year of publication of the first edition of the image compression standard known as JPEG 2000. JPEG 

refers to Joint Photographic Experts Group.
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VERSION HISTORY

Date Activity
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TABLE OF CONTENTS

1. Investigative Goals and Objectives .......................................................................................................................................................17

1.1. Background ...................................................................................................................................................................................17

1.2. Key Drivers and Mandates ............................................................................................................................................................18

2. Materials and Methods..........................................................................................................................................................................19

2.1. Compression Algorithms ..............................................................................................................................................................19

2.2. Compression Conditions ...............................................................................................................................................................21

3. Analysis.................................................................................................................................................................................................. 22

3.1. Performance Metrics ................................................................................................................................................................... 22

3.2. Uncertainty Measurement .......................................................................................................................................................... 23

3.3. Algorithmic Comparisons ............................................................................................................................................................ 24

3.4. Architecture and Implementation Comparisons ........................................................................................................................ 28

4. Results....................................................................................................................................................................................................31

4.1. Investigative Goal 1: Examine Effective Compression Rates by Algorithm ................................................................................31

4.2. Investigative Goal 2: Examine Compression Throughput by Algorithm .................................................................................... 32

4.3. Investigative Goal 3: Examine Decompression Throughput by Algorithm................................................................................ 33

4.4. Investigative Goal 4: Examine Effective Compression Rate by Impression Type...................................................................... 34

4.5. Investigative Goal 5: Comparison of JPEG 2000 Implementation With Respect to Effective Compression Rate ................... 35

4.6. Investigative Goal 6: Comparison of JPEG 2000 Implementation With Respect to Compression Throughput ......................36

4.7. Investigative Goal 7: Comparison of JPEG 2000 Implementation With Respect to Decompression Throughput................... 37

4.8. Investigative Goal 8: Comparison of Implementation Complexity ............................................................................................38

5. Conclusions ...........................................................................................................................................................................................39

6. Discussions and Future Work ...............................................................................................................................................................40

References ......................................................................................................................................................................................................41

Publications and Reports ...........................................................................................................................................................................41

Standards................................................................................................................................................................................................... 43

Appendix A. Dataset Makeup ..................................................................................................................................................................45

Appendix B. Equipment Used for Study..................................................................................................................................................50

Appendix C. Examination of Entropy .......................................................................................................................................................51

Procedure ...................................................................................................................................................................................................51

Results ....................................................................................................................................................................................................... 52
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LIST OF TABLES

Table 1 - Abbreviations.............................................................................................................................................................. 15

Table 2 –Impression Types Selected for This Study ................................................................................................................19

Table 3 - Compression Conditions.............................................................................................................................................21

Table 4 - Effective Compression Rates (Medians), Higher is Better....................................................................................... 31

Table 5 – Effective Compression, Wilcoxon Signed Rank Test, alpha of 0.05 ....................................................................... 31

Table 6 - Compression Throughput (Median Time, seconds), Lower is Better .....................................................................32

Table 7 - Compression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05 ...................................................................32

Table 8 - Decompression Throughput (Median Time, seconds).............................................................................................33

Table 9 - Decompression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05...............................................................33

Table 10 - Algorithm Performance Rankings (1-Best through 6-Worst).................................................................................34

Table 11 - Effective Compression Rates (Medians) by Implementation, Higher is Better.....................................................35

Table 12 - Effective Compression by Implementation, Wilcoxon Signed Rank Test, at alpha = 0.05....................................35

Table 13 - Compression Throughput (Median Time, seconds) by Implementation, Lower is Better ...................................36

Table 14 - Compression Throughput by Implementation, Wilcoxon Signed Rank Test, at alpha = 0.0033 ..........................36

Table 15 - Decompression Throughput (Median Time, seconds) by Implementation, Lower is Better ...............................37

Table 16 - Decompression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05 .............................................................37

Table 17 - Implementation Complexity ....................................................................................................................................38

Table 18 - Ink Card Scan Data classification by Impression Type ............................................................................................45

Table 19 - Ink Card Scan Pattern Classification for Single Finger Images by Impression Type..............................................45

Table 20 - Ink Card Scan Pattern Classification for Single Finger Images by Finger (Females)............................................ 46

Table 21 - Ink Card Scan Pattern Classification for Single Finger Images by Finger (Males) ................................................ 46

Table 22 – Digital Live Scan Data Classification by Impression Type ..................................................................................... 46

Table 23 – Digital Live Scan Pattern Classification for Single Finger Images by Impression Type........................................47

Table 24 – Digital Live Scan Pattern Classification for Single Finger Images by Finger (Females) .......................................47

Table 25 – Digital Live Scan Pattern Classification for Single Finger Images by Finger (Males) ...........................................47

Table 26 - Gender Breakdown for Data .................................................................................................................................. 48

Table 27 - Age Breakdown for Data ........................................................................................................................................ 48

Table 28 - Other Metadata: Height and Weight ..................................................................................................................... 48

Table 29 - Other Metadata: Eye Color..................................................................................................................................... 48

Table 30 - Image Geometry Data............................................................................................................................................. 49

Table 31 – Mean measurements of entropy and fingerprint region of images .....................................................................52
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LIST OF FIGURES

Figure 1 - Example of Fidelity Degradation Due to Extreme Lossy Compression (JPEG 2000 at 800:1)...............................18

Figure 2 - Normal Fit Histogram, Effective Compression (PNG)............................................................................................. 23

Figure 3 - Q-Q Normality Plot, Effective Compression (PNG) ................................................................................................. 23

Figure 4 - Effective Compression Rates (Medians)................................................................................................................. 25

Figure 5 - Compression Throughput (Median Time, seconds)................................................................................................26

Figure 6 - Decompression Throughput (Median Time, seconds............................................................................................. 27

Figure 7 - Effective Compression Rate (Median Ratio, by Platform and Implementation)...................................................28

Figure 8 - Compression Throughput (Median Time, seconds, by Platform and Implementation).......................................29

Figure 9 - Decompression Throughput (Median Time, seconds, by Platform and Implementation)...................................30
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TERMS AND DEFINITIONS

The abbreviations and acronyms of Table 1 are used in many parts of this document.

Table 1 - Abbreviations

BMP Bitmap File Format

bpp Bits per pixel

CR Compression Ratio

FBI Federal Bureau of Investigation

IAFIS Integrated Automated Fingerprint Identification System

IAI International Association for Identification

ICER Image Compression and Stereo Ranging

JPEG Joint Photographic Experts Group – ISO/IEC committee developing standards for image 

compression – also used as the name of the CODEC developed in accordance with the 

standard specified by this body.

NBIS NIST Biometric Image Software

NGI Next Generation Identification

NIST National Institute of Standards and Technology

OPJ OpenJPEG’s JPEG 2000 CODEC

PNG Portable Network Graphics

ppi Pixels per inch

ppmm Pixels per millimeter

PSNR Peak Signal To Noise Ratio

RLE Run Length Encoding

SIVV Spectral Image Validation/Verification Metric

WSQ Wavelet Scalar Quantization algorithm for compression of fingerprint imagery
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ABSTRACT

This paper presents the findings of a study initially conducted to measure the operational impact of JPEG 2000 lossy

compression on 1000 ppi fingerprint imagery at various levels of compression, but later expanded to include lossless 

compression. Lossless compression will have no impact on either Galton or non-Galton based features of a fingerprint 

since the compressed image is identical to the original once decompressed. The selection of a lossless compression 

algorithm can have operational implications in terms of effective compression rate and throughput; these implications 

are the focus. This study examines several such compression algorithms and compares them using criteria used to 

measure the effectiveness of the compression algorithm as well as its throughput using actual fingerprint imagery.

KEYWORDS

Fingerprint compression; 1000 ppi fingerprint imagery; JPEG 2000; JasPer; OpenJPEG; PNG; RLE; BMP; ICER; lossless 

compression
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1. Investigative Goals and Objectives

In July of 2009 NIST in partnership with the FBI commenced an investigation on the use of JPEG 2000 [JPEG2K] for 

compressing fingerprint imagery. Part one of this investigation, described in [NISTIR7778], addressed JPEG 2000 when 

operating with a non-reversible/lossy filter. The study described in this paper complements [NISTIR 7778] by examining 

the performance of several compression algorithms including JPEG 2000 when operating in a lossless fashion with the 

following investigative goals:

1. Examine Effective Compression Rates by Algorithm: Assess the performance of the selected algorithms 

according to how much effective compression they yield for various impression types.

2. Examine Compression Throughput by Algorithm: Assess the performance of the selected algorithms with 

respect to the time needed to generate a compressed representation of the original image.

3. Examine Decompression Throughput by Algorithm: Assess the performance of the selected algorithms with 

respect to the time needed to reconstruct the original image from the compressed representation.

4. Examine Effective Compression Rate by Impression Type: Identify if a particular impression type impacts 

effective compression rates more than other types.

5. Compare JPEG 2000 Implementations with Respect to Effective Compression Rate: Identify any operational 

differences in effective compression rate between various implementations of the JPEG 2000 algorithm for the 

same given set of input images.

6. Compare JPEG 2000 Implementations with Respect to Compression Throughput: Identify any operational 

differences in compression throughput between various implementations of the JPEG 2000 algorithm for the 

same given set of input images.

7. Compare JPEG 2000 Implementations with Respect to Decompression Throughput: Identify any operational 

differences in decompression throughput between various implementations of the JPEG 2000 algorithm3for 

the same given set of input images.

8. Compare Implementation Complexity: Examine and compare algorithm codebase size and complexity.

As an ancillary component of this study, the compression/decompression pathway was empirically verified to be truly 

lossless where the resulting compressed image stream would yield the exact original image, pixel-for-pixel.

1.1. Background

The criminal justice communities throughout the world exchange fingerprint imagery data primarily in 8-bit gray-scale 

and at 500 pixels per inch (ppi). The Wavelet Scalar Quantization (WSQ) [BRADLEY1], [BRADLEY2], [BRISLAWN], 

[HOPPER] image compression algorithm has been developed and maintained by the Federal Bureau of Investigation 

(FBI), Los Alamos National Laboratory (LANL) and the National Institute for Standards and Technology (NIST) as the 

standard for compressing 500 ppi fingerprint imagery in the United States. The WSQ standard defines a class of 

encoders and decoders with sufficient interoperability to ensure that images encoded by one compliant encoder can be 

decoded by any other compliant decoder. 

WSQ is a “lossy” compression algorithm. Lossy compression algorithms employ data encoding methods that discard

(lose) some of the data in the encoding process in order to achieve an aggressive reduction in the size of the data being 

compressed. Decompressing the resulting compressed data yields content that, while different from the original, is 

similar enough to the original that it remains useful for the intended purpose. Lossless compression algorithms, on the 

other hand, produce a compressed image that can be decompressed back to original form with no loss or change to the 

image. The disadvantage to lossless algorithms is that they produce compressed images that can be many times larger 

in file size than compressed images produced by lossy algorithms.

 

3

For purposes of the present study, both compression and decompression were performed by the same JPEG 2000 implementation. 

That is, there was no cross-over among implementations, e.g. encoding with one and decoding with another CODEC.
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The WSQ algorithm allows users to specify how much compression to apply, where higher amounts of compression 

result in a more compact representation but greater loss in fidelity to the original image.

Figure 1 below shows an example of such image degradation and fidelity loss as a result of lossy compression. The WSQ 

Gray-Scale Fingerprint Image Compression Specification [WSQ] provides guidance for the acceptable amount of fidelity 

loss due to compression in order for the encoder and decoder to meet FBI certifications for 500 ppi fingerprint imagery. 

These certifications are designed to ensure adherence to the WSQ specification to ensure sufficient fidelity for 

admissibility of images in courts of law that have been processed by such encoders and decoders. 

Original Image Compressed Image (Lossy)

Figure 1 - Example of Fidelity Degradation Due to Extreme Lossy Compression (JPEG 2000 at 800:1)

A study conducted by the International Association for Identification (IAI) [FITZPATRICK] established 15:1 as a WSQ 

compression ratio that would retain acceptable image fidelity in 500 ppi fingerprint imagery. The study used the 

judgments of expert fingerprint examiners to measure the fidelity loss due to compression. In order to reduce bias due 

to subjectivity, multiple examiner decisions were used to build consensus. Utilizing examiners’ opinions does not imply 

that automated fingerprint matcher performance is not an important criterion in a given biometric system, but it must 

be noted that if fingerprints are to be admissible as evidence in a court of law their ultimate utility lies in the expert 

examiner’s opinion of the fidelity of those fingerprints.

1.2. Key Drivers and Mandates

In modernizing its environment as part of the Next Generation Identification (NGI) initiative, the FBI seeks to expand its 

ability to exchange fingerprints at 1000 ppi in an effort to improve upon the capacity of systems in fingerprint 

identification and verification tasks and meet the FBI mandate to:

- Protect the United States from terrorist attack, foreign intelligence operations and espionage

- Support federal, state, local and international partners in their efforts to prevent or reduce crime and violence

- Upgrade technology to support the FBI's missions

Toward meeting these goals, the FBI seeks to set guidance for the next generation encoders and decoders based on 

the JPEG 2000 compression standard [JP2K] in order to ensure interoperability, fidelity and admissibility for 1000 ppi 

images in courts of law in the criminal justice community. While this is the case for lossy compression, other CODECs 

might be considered for lossless compression of fingerprints for which the desire is to maintain absolute fidelity of the 

image to the original, yet achieve some savings of storage space. 

In support of the FBI, the National Institute of Standards and Technology (NIST) conducted a study to determine an 

optimal compression approach that follows on the IAI study of WSQ compression for 500 ppi fingerprint imagery, to 
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build upon existing guidance for JPEG 2000 compression of fingerprint imagery, and to formulate a basis with which a 

normative compression guidance can be established in the ANSI/NIST standard for biometric data interchange 

[AN2011]. 

NIST has an established expertise in evaluating biometric systems and standards, and has been assigned by the USA 

PATRIOT Act (Public Law 107-56) the responsibility for developing and certifying biometric technology standards. NIST 

has been supporting biometric standards and evaluation activities for over forty years, starting with automated 

fingerprint analysis which began in 1965.

The MITRE Corporation has developed an informative guidance that is widely recognized as the de facto standard 

guidance for utilizing JPEG 2000 for the compression of 1000 ppi fingerprint imagery in MTR-04B0000022 [MTR]. While 

this document provides a compression profile for 1000 ppi fingerprint imagery using JPEG 2000, the guidance focuses 

on lossy compression of fingerprints. Unlike WSQ, JPEG 2000 supports both lossy functionality similar to WSQ (see 1.1), 

as well as a lossless mode. This study examines JPEG 2000 in lossless mode and compares its performance to other 

lossless compression algorithms. 

2. Materials and Methods

A total of 1400 images were selected for this study from the NIST SD-27 special database [SD27]. These images included 

various impression types (see Table 2) and included multiple subjects (see Appendix A for more information on the 

makeup of the input data set). The images were then processed by the algorithms selected for this study. The 

algorithms selected were instrumented specifically for this study to enable the collection of timing data. Effective 

compression rate data was calculated from the resulting compressed files at a later time. No specific protocol was put 

in place for the ordering of images in the input set, however the images were processed in an arbitrarily stratified 

fashion in the order of case numbers 2, 4, 1, 3, 6, 5 and 7.

Each algorithm was tested with all the images independently and the images were processed sequentially without any 

delays or throttling of the processing stages.

Table 2 –Impression Types Selected for This Study

Case Number Data Medium Impression Type Count

1 Ink Card Scan Rolled single finger 200

2 Ink Card Scan Flat single finger 200

3 Ink Card Scan Slap-four finger 200

4 Ink Card Scan Latent Lift Image 200

5 Digital Live Scan Rolled single finger 200

6 Digital Live Scan Flat single finger 200

7 Digital Live Scan Slap-four finger 200

Total: 1,400

2.1. Compression Algorithms

The focus of this study is to examine the effectiveness of various lossless compression algorithms on 1000 ppi 

fingerprint images. While there are many suitable algorithms, this study focuses on five: two implementations of JPEG 

2000 (OpenJPEG and JasPer [JPEG2K]), PNG, RLE used in BMP, and ICER. JPEG 2000 (lossless) and PNG were included in 

this selection set because they are currently part of the existing standard for fingerprint data exchange [AN2011]. 

Although the ANSI/NIST standard also lists lossless JPEG, this format was not examined in this study due to anecdotal 

evidence indicating the low rate of acceptance of this compression format for 500 ppi fingerprint images. RLE is one of 
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the oldest and simplest compression algorithms and was included as a baseline due to its simple algorithm design and 

wide implementation availability. ICER is quite new, and was selected to represent a robust state-of-the art approach.

All algorithms were tested on the same machine (See Appendix B for test machine configuration) to eliminate any 

computing platform bias.

2.1.1. JPEG 2000

JPEG 2000 is an image compression standard and coding system that was created by the Joint Photographic Experts 

Group committee (JPEG) in 2000 to improve on the original JPEG image compression standard’s discrete cosine 

transform-based methodology [JPEG] by utilizing a wavelet-based methodology. In addition to providing a lossy 

approach, JPEG 2000 also provides for a lossless/reversible filter.

2.1.2. PNG

Portable Network Graphics (PNG) is an image compression standard and coding system that was created by the PNG 

Working Group and later accepted as a standard in 2004 (ISO/IEC 15948:2004) [ISO/IEC]. PNG was primarily created to 

improve upon and replace GIF (Graphics Interchange Format) as an open image file format not requiring a patent 

license. PNG utilizes a 2-stage compression process where a prediction filter is applied to the image in an attempt to 

make the data more compressible in the ultimate compression stage. PNG is a very flexible compression algorithm 

supporting palette-based color images, grayscale images, and RGB images. 

2.1.3. RLE Used in BMP

Run-Length Encoding (RLE) is a very simple form of data compression used by the BMP file format in which sequences 

of repetitive data are stored as a single data value with an associated repeat count for that value, rather than as the 

original sequence. This compression algorithm is most useful on data that contains many such repeating sequences of 

the same value, for example a largely blank image containing only white pixels. RLE is not effective with files that 

exhibit any significant entropy4in structure, and frequently the file size of such data can greatly increase after being 

compressed with RLE. This is due to the addition of count values to the byte stream when few data are actually 

repeated, resulting in more data added than removed or compressed. 

2.1.4. ICER 

ICER (Image Compression and Stereo Ranging) [KIELY1],[KIELY2] is a modern wavelet-based image data compression 

algorithm specially designed to meet the needs of deep-space exploration applications such as the Mars Exploration 

Rover (MER) where state-of-the-art data compression effectiveness and efficiency are primary concerns rather than 

catering to general purpose applications. ICER provides lossless and lossy compression modes. For the purposes of this 

study it was examined in lossless mode. While ICER is not generally available for commercial use, it has been included as 

a reference modern high-end compression algorithm, just as RLE has been included in this study to provide a reference 

low-end compression algorithm.

 

4 Appendix C provides a comprehensive description of entropy as interpreted in this study.
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2.2. Compression Conditions

Per the investigative goals described in Section 1, this study’s experimental goals can be differentiated into two 

overarching tracks each consisting of 6 compression conditions (see Table 3 below). The first track focuses on the 

comparison of different compression algorithms (inter-algorithm conditions) while the second track focuses on the 

different implementations of the same algorithm (intra-algorithm, JPEG-2000 specifically).

Table 3 - Compression Conditions

Experimental Condition (Inter-Algorithm) Experimental Conditions (Intra-Algorithm)

Non-Compressed OpenJPEG v.1.3, 32-bit

RLE (32-bit) OpenJPEG v.1.3, 64-bit

PNG (32-bit) OpenJPEG v.1.4, 32-bit

JPEG 2000 (OpenJPEG v.1.4, 32-bit) OpenJPEG v.1.4, 64-bit

JPEG 2000 (JasPer 1.900.1, 32-bit) JasPer 1.900.1 32-bit

ICER (32-bit) JasPer 1.900.1 64-bit
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3. Analysis

3.1. Performance Metrics

In the case of compression performance measurement, there are three key factors in establishing overall performance. 

These factors include the effective compression ratio of the algorithm, the effective throughput of the algorithm when 

it compresses the image, and the effective throughput of the algorithm when it decompresses the image. 

3.1.1. Effective Compression Ratio Measurement

The effective compression ratio is measured by how much size reduction is obtained relative to the original noncompressed image by applying the algorithm being studied. In examining effective compression across multiple test 

images of various sizes and impression type, as was the case in this study, simply comparing resulting image sizes is not 

practical as the input images are not of homogenous geometry prior to compression (See Table 30). A simple and 

common metric of effective compression is defined by calculating the ratio of the non-compressed (raw) image size to

the compressed image size as defined below:

 

 

 

3.1.2. Effective Compression Throughout

As with other metrics, the effective compression throughput of an algorithm can be measured in many ways such as 

including or excluding IO overhead and disk cache effectiveness. For the scope of this study a simple clock-based 

approach on a dedicated machine was employed. This is described further in Appendix B. The compression time was 

measured by instrumenting each of the compression algorithms to record the difference in the time interval from

launch to when the compressed image has been saved onto a hard disk. No attempt was made to exclude and/or 

measure disk IO overhead or to measure any impact of built in disk caching mechanism on compression throughput. All 

testing was performed on a single machine to avoid any bias introduced by differences in architecture and 

configuration between multiple machines. 

3.1.3. Effective Decompression Throughput

The effective decompression throughput of an algorithm was measured similarly to the measurement of effective 

compression throughput. The decompression time was measured by instrumenting each of the decompression 

algorithms to record the difference in the computer’s real-time-clock from launch to when the image had been 

successfully decompressed and saved onto a hard disk. No attempt was made to exclude and/or measure disk IO

overhead or to measure any impact the built in disk caching mechanism has on decompression throughput. All testing 

was performed on a single machine to avoid any bias introduced by differences in architecture and configuration 

between multiple machines.
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3.2. Uncertainty Measurement

3.2.1. Normality Test

Examination of the distribution of data in the normal probability plot [CHAMBERS] and the Q-Q Plot on a selected

subset of data from this study (effective compression ratio performance of the PNG algorithm) in Figure 2 and Figure 3 

shows that it is not possible to determine the distribution of the data and therefore parametric analysis methods are 

not suitable. Because of this we accept the median as the comparison statistic and utilize a bootstrap method which 

does not rely on the distribution of the underlying data to assess the uncertainty of the medians estimated from the 

observed sample measurements.

Figure 2 - Normal Fit Histogram, Effective 

Compression (PNG)

Figure 3 - Q-Q Normality Plot, Effective 

Compression (PNG)

3.2.2. Bootstrap Procedure

Given the observed non-normality of the measurements, we employ the sample median of N=1400 measurements of 

each compression metric (or N=200 measurements for each of seven different impression types) as our comparison 

statistic. We estimate the uncertainty, i.e., standard error, of the median using a bootstrap procedure [WU1], [WU2], 

[WU3] wherein we re-compute the median for each of 1,000 samples of size N, sampled randomly with replacement 

from the original N measurements. The 95 % confidence interval for the median statistic is then taken as the 0.025th and 

0.975th quantiles of the distribution of median replicates. The sample medians and their upper and lower confidence 

limits are shown in figures 4 – 9 in sections below. 

0

200

400

600

800

1000

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Frequency

Effective Compression Ratio (PNG, N:1)

Histogram

Normal Fit

(Mean=1.7578, SD=0.8641)

-4

-3

-2

-1

0

1

2

3

4

5

1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Normal Quantile (Z)

Effective Compression Ratio (PNG, N:1)

Normality Plot (Q-Q)

Normal Fit

(Skewness=1.56, Kurtosis=2.06)

(W = 0.78, p = 0.0000)
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PAGE 24 OF 52

3.2.3. Hypothesis Testing

Hypothesis testing is performed using a non-parametric method, specifically the Wilcoxon Signed Rank Test 

[WILCOXON], [HOLLANDER]. Differences among the various compression processes are tested for each of three 

metrics. We test for differences in each of three metrics between every pair of compression processes without regard 

to directionality of differences (two-tailed test). We have two sets of compression conditions (Inter-Algorithm and 

Intra-Algorithm described in Section 2.2) under test yielding for each set 15 pairwise comparisons for each of the three

comparison metrics (effective compression rate, compression throughput and decompression throughput). We also 

perform the pairwise comparison tests for each of the p = 10 fingerprint groupings including All Data, Latent, Ink Card 

Scan All, Ink Card Scan Rolled, Ink Card Scan Flat, Ink Card Scan Slap, Digital Live Scan All, Digital Live Scan Rolled, Digital 

Live Scan Flat, and Digital Live Scan Slap. That is, for each pair of compression treatments, we wish to test the null 

hypothesis, H0, that the median difference of pairwise measurements is zero. We reject the null hypothesis if we find 

the probability of its truth to be less than the Type I error rate, i.e. the probability of incorrectly rejecting the null 

hypothesis. Typically, the acceptable Type I error rate is set at 5%, i.e. α = 0.05. If we are able to reject the null 

hypothesis, we accept the alternative hypothesis, H1, that the median of N pairwise differences in measurements is not 

equal to zero. Thus, for the present experiments, the null hypotheses, H0, and alternative hypotheses, H1, for the n=15

pairwise comparisons may be stated mathematically as: 

 

 

, , , , , ,

, , . , , ,

) 0

) 0

i c k g i d k g

i c k g i d k g









i=1...N;c=1...6;d=1...6(d c;);k=1...3;g=1...9 c,k,g

0

c,k,g

i=1...N;c=1...6;d=1...6(d c);k=1...3;g=1...9 c,k,g

1

c,k,g

H : Med(m - m

H : Med(m - m

where m in the expression denotes a metric further specified by subscripts; i designates a pair of measurements 1...N; c

designates the compression method 1...6; k designates metric 1...3; and g designates fingerprint groupings 1...9. Each 

of the paired tests is independent of other tests and the procedure is performed separately for each metric and for 

each group of fingerprint images as specified above. Moreover, in a similar analysis, we compare various 

implementations of the JPEG 2000 CODEC.

The Wilcoxon Signed Rank test examines differences between pairwise measurements without requiring the 

assumption of normality. This test is analogous to the pairwise t-test used to compare pairs of measurements having 

distributions known to satisfy the assumptions of normality. Even as subsequent tables and graphs present sample 

medians, the hypothesis test comparisons are made between pairs of measurements and not between distributions.

3.3. Algorithmic Comparisons

The first phase of this study entailed comparison of the various algorithms selected for this study and their relative 

differences. These differences can be summarized as the differences in the effectiveness of the algorithm in terms of 

compressing the image relative to the raw file size, the amount of time needed by the algorithm to compress the image 

and the amount of time needed by the algorithm to decompress that image.
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PAGE 25 OF 52

3.3.1. Effective Compression Rate Comparison

Comparison of median values for effective compression ratios shows that the wavelet-based compression algorithms 

(ICER and JPEG 2000) hold a definitive advantage over non-wavelet-based compression algorithms (PNG, RLE) in 

compressing mixed image types as shown in Figure 4. The older RLE algorithm in the BMP format appears to be 

negatively impacted by the high entropy of fingerprint data where it incurs a negative compression penalty; that is, the 

image grows in size after compression relative to the original non-compressed file. One exception emerges however, in 

that Digital Live Scan Rolled images achieve higher compression ratios using PNG in contrast to wavelet-based 

algorithms. Examination of image entropies shows that Digital Live Scan images typically contain less entropy relative 

to Ink Card Scan images, particularly in the case of untextured backgrounds. This renders these images more 

compressible using traditional (non-wavelet-based) algorithms.

Figure 4 - Effective Compression Rates (Medians)

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

BMP/RLE (32-bit) 0.5822 0.5793 0.5338 0.5202 0.5333 0.5495 1.2125 1.5448 0.7623 1.1908

Non-Compressed 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

PNG (32-bit) 1.3227 1.3137 1.1492 1.0715 1.1619 1.1928 2.4351 3.0325 1.5901 2.4235

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7331

JPEG 2000 (JasPer 1.900.1, 32-bit) 1.9551 2.3407 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

ICER (32-bit) 2.0148 2.4130 1.7459 1.5637 1.8148 1.7921 2.5645 2.9126 1.9343 2.7560

0.5 : 1 1.0 : 1 1.5 : 1 2.0 : 1 2.5 : 1 3.0 : 1 3.5 : 1

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Effective Compression Rate (Median Ratio)

ICER (32 bit)

JPEG 2000 (JasPer 1.900.1, 32 bit)

JPEG 2000 (OpenJPEG v.1.4, 32 bit)

PNG (32 bit)

Non-Compressed

BMP/RLE (32 bit)
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3.3.2. Compression Throughput Comparison

In addition to effective compression ratio, the time an algorithm takes to create a compressed representation of an 

image can also be a significant factor in the selection of an algorithm. The most notable example here is mobile 

applications where image compression may take place on a device with modest computational resources. It should also 

be noted that in common biometric processing workflows, the compression process typically occurs once after image 

acquisition, while the image may be decompressed one or more times; therefore the load incurred during compression 

is a transient one. Comparison of median values for effective compression time has been provided in Figure 5. 

Throughput data shows that while the compression time varies heavily by algorithm implementation, there is some 

stratification both by algorithm and image geometry (size). In the case of JPEG 2000, the two implementations of JPEG 

2000 (JasPer and OpenJPEG) show significant differences in algorithm compression throughput. Examination of 

algorithm throughputs does not show a consistent advantage towards either wavelet or non-wavelet-based

compression algorithms, but does show that compression throughput can vary greatly by specific implementations of 

the algorithm.

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

RLE (32-bit) 0.1651 0.1151 0.1329 0.0889 0.0671 0.2848 0.1836 0.1189 0.0737 0.2775

PNG (32-bit) 0.6638 0.9194 0.6733 0.6068 0.2976 3.1707 0.5211 0.5188 0.2956 1.5004

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 1.0878 1.1219 0.9754 0.8274 0.3831 4.0815 1.2043 1.2043 0.4728 3.3437

JPEG 2000 (JasPer 1.900.1, 32-bit) 0.4188 0.4261 0.3916 0.3339 0.1548 1.6176 0.4378 0.4378 0.1764 1.1896

ICER (32-bit) 0.5665 0.5490 0.4098 0.3490 0.1606 1.8771 0.6475 0.6475 0.1888 1.7206

Figure 5 - Compression Throughput (Median Time, seconds)

0.0s 0.5s 1.0s 1.5s 2.0s 2.5s 3.0s 3.5s 4.0s 4.5s

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Compression Throughput (Median Time, seconds)

ICER (32 bit)

JPEG 2000 (JasPer 1.900.1, 32 bit)

JPEG 2000 (OpenJPEG v.1.4, 32 bit)

PNG (32 bit)

BMP/RLE (32 bit)
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3.3.3. Decompression Throughput Comparison

Another important factor in algorithm performance is the amount of time the algorithm takes to decompress the image 

for processing and/or viewing. The decompression time can be a significant factor in the selection of an algorithm as it 

can occur multiple times in the lifecycle of the biometric record. Comparison of median values for effective 

decompression time has been provided in Figure 6. Throughput data shows that while the compression time varies 

heavily by algorithm implementation, the non-wavelet-based compression algorithms (PNG, RLE) show an advantage in 

decompression throughput. This advantage is most dramatic with PNG. In the case of JPEG 2000, the two 

implementations of JPEG 2000 (JasPer and OpenJPEG) show significant differences in algorithm decompression 

throughput suggesting differing levels of optimization between specific implementations of the algorithm.

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

RLE (32-bit) 0.2317 0.2680 0.2423 0.2121 0.1147 0.7211 0.1686 0.1479 0.0843 0.4508

PNG (32-bit) 0.0525 0.0566 0.0491 0.0416 0.0364 0.1576 0.0518 0.0509 0.0343 0.1011

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 0.8499 0.8549 0.7269 0.6304 0.2933 3.1451 0.9534 0.9534 0.3414 2.6300

JPEG 2000 (JasPer 1.900.1, 32-bit) 0.3660 0.3649 0.3428 0.2857 0.1323 1.3939 0.3914 0.3914 0.1575 1.0549

ICER (32-bit) 0.9471 0.9294 0.6438 0.5553 0.2794 2.9762 1.1068 1.1068 0.3218 2.8767

Figure 6 - Decompression Throughput (Median Time, seconds

0.0s 0.5s 1.0s 1.5s 2.0s 2.5s 3.0s 3.5s 4.0s 4.5s

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Decompression Throughput (Median Time, seconds)

ICER (32 bit)

JPEG 2000 (JasPer 1.900.1, 32 bit)

JPEG 2000 (OpenJPEG v.1.4, 32 bit)

PNG (32 bit)

BMP/RLE (32 bit)
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PAGE 28 OF 52

3.4. Architecture and Implementation Comparisons

The second phase of this study focused on the JPEG 2000 algorithm and examined the relative differences between the 

various implementations and architectural configurations of this algorithm. Again, as with section [3.3] the differences 

are summarized as the differences in the effectiveness of the algorithm in terms of compressing the image relative to 

the raw (non-compressed) file size, the amount of time needed by the algorithm to compress the image and the 

amount of time needed by the algorithm to decompress that image.

3.4.1. Effective Compression Rate Comparison

Comparison of median values for effective compression ratios shows that the various implementations of JPEG 2000

yield nearly identical compression rates. 

CODEC Image Type

All Data Ink Card Scan Digital Live Scan

All Latent All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 1.9550 All 1.6978 1.5098 1.7607 1.7420 2.5555 2.9099 1.8950 2.7331

OpenJPEG v.1.3, 64-bit 1.9550 2.3406 1.6978 1.5098 1.7607 1.7420 2.5555 2.9099 1.8950 2.7331

OpenJPEG v.1.4, 32-bit 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7332

OpenJPEG v.1.4, 64-bit 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7332

JasPer 1.900.1 32-bit 1.9551 2.3406 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

JasPer 1.900.1 64-bit 1.9551 2.3407 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

Figure 7 - Effective Compression Rate (Median Ratio, by Platform and Implementation)

0.5 : 1 1.0 : 1 1.5 : 1 2.0 : 1 2.5 : 1 3.0 : 1 3.5 : 1

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Effective Compression Rate (Median Ratio)

OpenJPEG v.1.3, 32 bit

OpenJPEG v.1.3, 64 bit

OpenJPEG v.1.4, 32 bit

OpenJPEG v.1.4, 64 bit

JasPer 1.900.1 32 bit

JasPer 1.900.1 64 bit
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3.4.2. Compression Throughput Comparison

Comparison of median values for effective compression time as shown in Figure 8 shows that platform optimization 

and evolution can lead to significant improvements in speed as evident from comparing OpenJPEG v.1.3 and v.1.4. 

Architectural differences however are not evident. For example, the JasPer implementation shows a performance 

penalty when compiled for a 64-bit architecture, while OpenJPEG appears to have performance gains when operating 

in a 64-bit environment.

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 1.0506 1.0771 0.9126 0.7956 0.3762 3.8532 1.1811 1.1801 0.4614 3.2632

OpenJPEG v.1.3, 64-bit 0.9940 1.0120 0.8658 0.7332 0.3515 3.7440 1.0980 1.0980 0.4266 3.1001

OpenJPEG v.1.4, 32-bit 1.0757 1.0995 0.9282 0.7800 0.3732 4.0248 1.1775 1.1775 0.4527 3.3267

OpenJPEG v.1.4, 64-bit 0.4700 0.4873 0.4042 0.3427 0.1641 1.7316 0.5130 0.5130 0.1904 1.4411

JasPer 1.900.1 32-bit 0.4147 0.4289 0.3748 0.3234 0.1558 1.6068 0.4314 0.4314 0.1752 1.2050

JasPer 1.900.1 64-bit 0.6708 0.7075 0.5928 0.4991 0.2434 2.5116 0.7152 0.7152 0.2846 2.0416

Figure 8 - Compression Throughput (Median Time, seconds, by Platform and Implementation)

0.0s 0.5s 1.0s 1.5s 2.0s 2.5s 3.0s 3.5s 4.0s 4.5s

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Compression Throughput (Median Time, seconds)

OpenJPEG v.1.3, 32 bit

OpenJPEG v.1.3, 64 bit

OpenJPEG v.1.4, 32 bit

OpenJPEG v.1.4, 64 bit

JasPer 1.900.1 32 bit

JasPer 1.900.1 64 bit
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3.4.3. Decompression Throughput Comparison

Comparison of median values for effective decompression time as shown in Figure 9 shows a similar pattern to the data 

collected in compression throughput measurements from Figure 8. Again, platform optimization and evolution appear 

to lead to significant improvements in decompression speed as evident from comparing OpenJPEG v.1.3 and v.1.4. 

Architectural differences do not appear to significantly or consistently impact decompression times. For example, the 

JasPer implementation shows a throughput penalty when operating in 64-bit compilation, while OpenJPEG appears to 

have performance gains when operating in 64-bit compilation.

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 0.9203 0.9399 0.7877 0.6785 0.3245 3.4319 1.0434 1.0434 0.3958 2.9190

OpenJPEG v.1.3, 64-bit 0.9029 0.9206 0.7954 0.6707 0.3216 3.4006 0.9984 0.9984 0.3893 2.8150

OpenJPEG v.1.4, 32-bit 0.8578 0.8734 0.7174 0.6083 0.2964 3.1198 0.9494 0.9494 0.3482 2.6870

OpenJPEG v.1.4, 64-bit 0.3587 0.3749 0.3086 0.2617 0.1236 1.3258 0.3930 0.3930 0.1435 1.1019

JasPer 1.900.1 32-bit 0.3631 0.3635 0.3302 0.2871 0.1344 1.3726 0.3859 0.3859 0.1566 1.0729

JasPer 1.900.1 64-bit 0.5728 0.5874 0.4994 0.4320 0.2081 2.1527 0.6179 0.6179 0.2404 1.7299

Figure 9 - Decompression Throughput (Median Time, seconds, by Platform and Implementation)

0.0s 0.5s 1.0s 1.5s 2.0s 2.5s 3.0s 3.5s 4.0s 4.5s

All Data

Digital Live Scan - Latent

Ink Card Scan - All

Ink Card Scan - Rolled

Ink Card Scan - Flat

Ink Card Scan - Slap

Digital Live Scan - All

Digital Live Scan - Rolled

Digital Live Scan - Flat

Digital Live Scan - Slap

Decompression Throughput (Median Time, seconds)

OpenJPEG v.1.3, 32 bit

OpenJPEG v.1.3, 64 bit

OpenJPEG v.1.4, 32 bit

OpenJPEG v.1.4, 64 bit

JasPer 1.900.1 32 bit

JasPer 1.900.1 64 bit
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4. Results

4.1. Investigative Goal 1: Examine Effective Compression Rates by Algorithm

One of the primary drivers in the selection of a compression ratio is the effective compression rate obtained using that 

algorithm on the data of interest. The first investigative goal of this study is to determine if any particular algorithm 

holds a definitive advantage versus the others, and if the measured advantage is statistically significant.

4.1.1. Investigative Analysis 1

Based on the median data (Table 4), ICER provides the best compression rate for mixed image types (“All Data”) with a

median ratio of 2.0148:1 followed by the implementations of JPEG 2000. Pair-wise examination of the data using 

Wilcoxon Signed Rank Test with a significance level (alpha) of 0.05 shows that the differences between ICER and the 

two JPEG 2000 algorithms are closer than other probabilities to the Type I error threshold of 0.05 for the Digital Live 

Scan case, but are still significantly different (Table 5). Hence the null hypothesis is rejected for this comparison as well 

as for all other comparisons for which the probability of incorrectly rejecting the null hypothesis are well below the 0.05 

level. The data also shows that ICER and JPEG 2000 yield the best compression performance for every case except for 

Digital Live Scan Rolled imagery where PNG outperforms all other algorithms.

Table 4 - Effective Compression Rates (Medians), Higher is Better

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

RLE (32-bit) 0.5822 0.5793 0.5338 0.5202 0.5333 0.5495 1.2125 1.5448 0.7623 1.1908

Non-Compressed 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

PNG (32-bit) 1.3227 1.3137 1.1492 1.0715 1.1619 1.1928 2.4351 3.0325 1.5901 2.4235

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7331

JPEG 2000 (JasPer 1.900.1, 32-bit) 1.9551 2.3407 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

ICER (32-bit) 2.0148 2.4130 1.7459 1.5637 1.8148 1.7921 2.5645 2.9126 1.9343 2.7560

Table 5 – Effective Compression, Wilcoxon Signed Rank Test, alpha of 0.05

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 Non-Compressed RLE <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

2 Non-Compressed PNG <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

3 Non-Compressed J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 Non-Compressed J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

5 Non-Compressed ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

6 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 RLE J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 RLE J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

9 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

10 PNG J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

11 PNG J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

12 PNG ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

13 J2K (OpenJPEG v.1.4) J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0013 <0.0001 <0.0001 <0.0001

14 J2K (OpenJPEG v.1.4) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0400 <0.0001 <0.0001

15 J2K (JasPer 1.900.1) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0426 <0.0001 <0.0001

4.1.2. Investigative Result 1

Based on the data, the wavelet algorithms provide consistently better performance than their non-wavelet-based

counterparts with results that are statistically significant. ICER leads the pack, followed by JPEG 2000. The only 

exception to this was the Live Scan Rolled image case. It is hypothesized that the lower entropy of this particular image 

type may make these images more effectively compressible by PNG. RLE on the other hand seems to suffer a significant 

penalty with high entropy images such as Ink Card Scan where the resulting compressed representation is larger than 

the original non-compressed images.
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PAGE 32 OF 52

4.2. Investigative Goal 2: Examine Compression Throughput by Algorithm

Another important driver in the selection of a compression ratio is throughput of the algorithm in terms of the time it 

takes to generate the compressed representation of data of interest. Compression typically only occurs once upon the 

collection and packaging of the biometric sample and therefore it is not perceived as a source of any significant 

recurring load on the processing pathways of the typical biometric system. Compression time however can be a 

significant decision driver for devices with modest computational ability such as hand-held or embedded devices where 

even a transient load may have very significant impact on the workflow and usability of such devices. The second 

investigative goal of this study is to determine if any particular algorithm holds a definitive advantage in terms of 

compression throughput, and if the measured advantage is statistically significant.

4.2.1. Investigative Analysis 2

Based on the median data (Table 6), RLE provides the best compression throughput5for mixed image types (“All 

Data”) with a median ratio of 0.1651 seconds, followed by the JasPer implementation of JPEG 2000 and PNG. Pair-wise 

examination of the data using Wilcoxon Signed Rank Test with a significance level (alpha) of 0.05 shows that all 

comparisons were statistically significant for all comparison pairs and image types, i.e. all probabilities are below the 

0.05 level.

Table 6 - Compression Throughput (Median Time, seconds), Lower is Better

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

RLE (32-bit) 0.1651 0.1151 0.1329 0.0889 0.0671 0.2848 0.1836 0.1189 0.0737 0.2775

PNG (32-bit) 0.6638 0.9194 0.6733 0.6068 0.2976 3.1707 0.5211 0.5188 0.2956 1.5004

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 1.0878 1.1219 0.9754 0.8274 0.3831 4.0815 1.2043 1.2043 0.4728 3.3437

JPEG 2000 (JasPer 1.900.1, 32-bit) 0.4188 0.4261 0.3916 0.3339 0.1548 1.6176 0.4378 0.4378 0.1764 1.1896

ICER (32-bit) 0.5665 0.5490 0.4098 0.3490 0.1606 1.8771 0.6475 0.6475 0.1888 1.7206

Table 7 - Compression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 Non-Compressed RLE <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

2 Non-Compressed PNG <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

3 Non-Compressed J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 Non-Compressed J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

5 Non-Compressed ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

6 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 RLE J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 RLE J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

9 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

10 PNG J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

11 PNG J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

12 PNG ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

13 J2K (OpenJPEG v.1.4) J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

14 J2K (OpenJPEG v.1.4) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

15 J2K (JasPer 1.900.1) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4.2.2. Investigative Result 2

The RLE (using BMP) algorithm provides it with the greatest throughput advantage for the mixed image type case (“All 

Data”). The JasPer implementation of JPEG 2000 and ICER follow RLE in throughput performance. It is hypothesized 

that the simplicity of the RLE’s compression is most likely responsible for the very high throughput, albeit at the cost of 

far less effective compression yield (See 4.1.1). The ordering of throughput is consistent with all image type cases, and 

statistically significant for every case.

 

5

It should be noted that the Non-Compressed case was omitted from this ranking. The Non-Compressed case, if taken in literal 

context with the other algorithms, would rank as best in compression throughput due to an elapsed compression time of 0. 
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4.3. Investigative Goal 3: Examine Decompression Throughput by Algorithm

Other than compression ratio, the decompression throughput of an algorithm is the next most important factor in 

terms of the time it takes to reconstruct the original image from its compressed representation (decompression).

Image decompression is expected to occur at least one time along the processing pathway of a biometric system, and 

possibly many times throughout the lifecycle of that biometric sample; therefore it may be a significant decision driver.

This non-transient load may again be a significant factor with portable devices. The third investigative goal of this study 

is to determine if any particular algorithm holds a definitive advantage versus the others in terms of decompression 

throughput, and if the measured advantage is statistically significant.

4.3.1. Investigative Analysis 3

Based on the median data (Table 8), PNG provides the best decompression throughput for mixed image types case 

(“All Data”) with a median of 0.0525 seconds, followed by RLE and the JasPer implementation of JPEG 2000. Pair-wise 

examination of the data using Wilcoxon Signed Rank Test with an alpha of 0.05 shows that all comparisons were 

statistically significant for all comparison pairs and image types, with probabilities of a Type I error well below the 0.05 

level.

Table 8 - Decompression Throughput (Median Time, seconds)

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

RLE (32-bit) 0.2317 0.2680 0.2423 0.2121 0.1147 0.7211 0.1686 0.1479 0.0843 0.4508

PNG (32-bit) 0.0525 0.0566 0.0491 0.0416 0.0364 0.1576 0.0518 0.0509 0.0343 0.1011

JPEG 2000 (OpenJPEG v.1.4, 32-bit) 0.8499 0.8549 0.7269 0.6304 0.2933 3.1451 0.9534 0.9534 0.3414 2.6300

JPEG 2000 (JasPer 1.900.1, 32-bit) 0.3660 0.3649 0.3428 0.2857 0.1323 1.3939 0.3914 0.3914 0.1575 1.0549

ICER (32-bit) 0.9471 0.9294 0.6438 0.5553 0.2794 2.9762 1.1068 1.1068 0.3218 2.8767

Table 9 - Decompression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 Non-Compressed RLE <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

2 Non-Compressed PNG <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

3 Non-Compressed J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 Non-Compressed J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

5 Non-Compressed ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

6 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 RLE J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 RLE J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

9 RLE ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

10 PNG J2K (OpenJPEG v.1.4) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

11 PNG J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

12 PNG ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

13 J2K (OpenJPEG v.1.4) J2K (JasPer 1.900.1) <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

14 J2K (OpenJPEG v.1.4) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

15 J2K (JasPer 1.900.1) ICER <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4.3.2. Investigative Result 3

PNG’s advantage in decompression throughput is dramatic and at times an order of magnitude better than the next 

closest performer. While PNG’s compression effectiveness may lag behind its wavelet-based counterparts (JPEG 2000 

and ICER), the performance advantages in decompression may make it a suitable algorithm for asymmetric use cases 

where decompression performance is critical.
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4.4. Investigative Goal 4: Examine Effective Compression Rate by Impression Type

Another factor in the selection of a compression algorithm is applicability of that algorithm to the taxonomy of the data 

that is expected as part of the workflow and processes. If the data has skewed taxonomy (i.e., only Flat images taken 

by a Digital Live Scan device) it may be beneficial to select a compression algorithm that performs better given that 

data. The fourth investigative goal of this study is to determine if any particular algorithm holds a definitive advantage 

versus the others in terms of performance by the type of image processed.

4.4.1. Investigative Analysis 4

The algorithms in this study were ranked in terms of performance for the various criteria of interest (effective 

compression ratio, compression throughput and decompression throughput). The results are provided in Table 10

below. Examination of rankings across the columns of Table 10 shows that rankings/stratification of algorithms appear 

to be relatively stable and consistent by various image types. There are cases where positions of algorithms with similar 

performance rankings switch, but this change in ranking is usually limited to algorithms with adjacent performance 

rankings. The notable exception to this is PNG’s effective compression ratio performance with Digital Live Scan Rolled

images where the algorithm jumps from a typical ranking of 4th place to 1st place relative to the other algorithms. For 

reference purposes, the non-compressed case has been included in the throughput rankings. The non-compressed 

cases here represent the system baseline of zero time required.

Table 10 - Algorithm Performance Rankings (1-Best through 6-Worst)

4.4.2. Investigative Result 4

Across all image types, the rankings for the various measurement criteria appear to be stratified in a stable fashion with 

very few changes in rankings6, and those changes in rankings are typically only a one position change in ranking (i.e., in 

terms of effective compression ratio RLE ranks 5

th on Digital Live Scan Slap but drops to 6th for Digital Live Scan Flat). 

The one exception is PNG for Digital Live Scan Rolled images where PNG jumps from 4th place ranking for all other 

image types to 1st place ranking. 

 

6 When examining the ranking data in this section, it would be prudent to examine the actual values of the metric in 

question as a change in rank order may be the result of a very small change in the underlying metric.

Factor CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

Effective Ratio

Non-Compressed 5 5 5 5 5 5 6 6 5 6

BMP/RLE (32 bit) 6 6 6 6 6 6 5 5 6 5

PNG (32 bit) 4 4 4 4 4 4 4 1 4 4

JPEG 2000 (OpenJPEG v.1.4, 32 bit) 3 3 3 3 3 3 2 3 3 2

JPEG 2000 (JasPer 1.900.1, 32 bit) 2 2 2 2 2 2 3 4 2 3

ICER (32 bit) 1 1 1 1 1 1 1 2 1 1

Compression Throughput

Non-Compressed 1 1 1 1 1 1 1 1 1 1

BMP/RLE (32 bit) 2 2 2 2 2 2 2 2 2 2

PNG (32 bit) 5 5 5 5 5 5 4 4 5 4

JPEG 2000 (OpenJPEG v.1.4, 32 bit) 6 6 6 6 6 6 6 6 6 6

JPEG 2000 (JasPer 1.900.1, 32 bit) 3 3 3 3 3 3 3 3 3 3

ICER (32 bit) 4 4 4 4 4 4 5 5 4 5

Decompression Throughput

Non-Compressed 1 1 1 1 1 1 1 1 1 1

BMP/RLE (32 bit) 3 3 3 3 3 3 3 3 3 3

PNG (32 bit) 2 2 2 2 2 2 2 2 2 2

JPEG 2000 (OpenJPEG v.1.4, 32 bit) 5 5 6 6 6 6 5 5 6 5

JPEG 2000 (JasPer 1.900.1, 32 bit) 4 4 4 4 4 4 4 4 4 4

ICER (32 bit) 6 6 5 5 5 5 6 6 5 6
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4.5. Investigative Goal 5: Comparison of JPEG 2000 Implementation With Respect to Effective 

Compression Rate

With certain algorithms, performance of the algorithm can vary greatly by the specific implementation of that 

algorithm (such as different vendors producing compatible but different algorithms), or the architecture for which the 

algorithm was built (such as 32-bit vs. 64-bit). The fifth investigative goal of this study is to see if such implementation 

differences do indeed introduce significant operational differences in terms of effective compression ratio. Since the 

focus of this study is the JPEG 2000 algorithm, this examination was limited to lossless JPEG 2000 and does not include 

RLE, ICER or PNG.

4.5.1. Investigative Analysis 5

The median compression rate data in Table 11 shows that the various implementations of JPEG 2000 performed nearly 

identically. Pair-wise examination of the data using the Wilcoxon Signed Rank Test with an alpha of 0.05 however 

shows significant differences at values which appear to be identical. Further examination of the statistical processes 

used have lead the team to conclude that pairwise analysis of real numbers may not be ideal for comparisons of nearly 

identical real values where subtle but operationally insignificant differences can lead to statistically significant 

differences between two such lists of values. A further discussion of this effect is provided in Section 6 of this report.

Table 11 - Effective Compression Rates (Medians) by Implementation, Higher is Better

CODEC All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 1.9550 2.3406 1.6978 1.5098 1.7607 1.7420 2.5555 2.9099 1.8950 2.7331

OpenJPEG v.1.3, 64-bit 1.9550 2.3406 1.6978 1.5098 1.7607 1.7420 2.5555 2.9099 1.8950 2.7331

OpenJPEG v.1.4, 32-bit 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7332

OpenJPEG v.1.4, 64-bit 1.9551 2.3406 1.6978 1.5099 1.7608 1.7420 2.5555 2.9100 1.8950 2.7332

JasPer 1.900.1 32-bit 1.9551 2.3407 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

JasPer 1.900.1 64-bit 1.9551 2.3407 1.6978 1.5099 1.7609 1.7420 2.5555 2.9099 1.8951 2.7331

Table 12 - Effective Compression by Implementation, Wilcoxon Signed Rank Test, at alpha = 0.05

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.3, 64-bit 0.3173 1.0000 0.3173 1.0000 0.3173 1.0000 1.0000 1.0000 1.0000 1.0000

2 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

3 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001

5 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001

6 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001

9 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001

10 OpenJPEG v.1.4, 32-bit OpenJPEG v.1.4, 64-bit 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

11 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0012 <0.0001 <0.0001 <0.0001

12 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0012 <0.0001 <0.0001 <0.0001

13 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0012 <0.0001 <0.0001 <0.0001

14 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0012 <0.0001 <0.0001 <0.0001

15 JasPer 1.900.1 32-bit JasPer 1.900.1 64-bit 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

4.5.2. Investigative Result 5

Examination of various implementations of JPEG 2000 in both 32-bit and 64-bit architectures yielded nearly identical 

results in effective compression rates therefore architectural differences are not expected to be a factor in the 

adoption of a particular implementation of lossless JPEG 2000 CODECs.
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4.6. Investigative Goal 6: Comparison of JPEG 2000 Implementation With Respect to 

Compression Throughput

With certain algorithms, throughput of the algorithm can also vary greatly by the specific implementation of that 

algorithm (such as different vendors producing compatible but different algorithms), or the architecture for which the 

algorithm was built (such as 32-bit vs. 64-bit). The sixth investigative goal of this study is to see if such implementation 

differences do indeed introduce significant operational differences in terms of compression throughput. As with the 

previous section, since the focus of this study is the JPEG 2000 algorithm, this investigative goal was limited to lossless 

JPEG 2000 and does not include RLE, ICER or PNG.

4.6.1. Investigative Analysis 6

The median compression throughput data in Table 13 shows that there are differences evident between the various 

implementations of JPEG 2000. Pair-wise examination of the data using Wilcoxon Signed Rank Test with an alpha of 

0.05 also confirmed significant differences between the OpenJPEG 32-bit v1.3 and 32-bit v1.4 implementations for all 

cases except Ink Scan Flat, Digital Live Scan All and Digital Live Scan Rolled.

Table 13 - Compression Throughput (Median Time, seconds) by Implementation, Lower is Better

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 1.0506 1.0771 0.9126 0.7956 0.3762 3.8532 1.1811 1.1801 0.4614 3.2632

OpenJPEG v.1.3, 64-bit 0.9940 1.0120 0.8658 0.7332 0.3515 3.7440 1.0980 1.0980 0.4266 3.1001

OpenJPEG v.1.4, 32-bit 1.0757 1.0995 0.9282 0.7800 0.3732 4.0248 1.1775 1.1775 0.4527 3.3267

OpenJPEG v.1.4, 64-bit 0.4700 0.4873 0.4042 0.3427 0.1641 1.7316 0.5130 0.5130 0.1904 1.4411

JasPer 1.900.1 32-bit 0.4147 0.4289 0.3748 0.3234 0.1558 1.6068 0.4314 0.4314 0.1752 1.2050

JasPer 1.900.1 64-bit 0.6708 0.7075 0.5928 0.4991 0.2434 2.5116 0.7152 0.7152 0.2846 2.0416

Table 14 - Compression Throughput by Implementation, Wilcoxon Signed Rank Test, at alpha = 0.0033

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.3, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

2 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 0.0758 <0.0001 0.4226 0.2151 <0.0001 <0.0001

3 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

5 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

6 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

9 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

10 OpenJPEG v.1.4, 32-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

11 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

12 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

13 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

14 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

15 JasPer 1.900.1 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4.6.2. Investigative Result 6

Examination of various implementations and architectures of JPEG 2000 yielded statistically significant differences in 

compression throughput between OpenJPEG 32-bit v1.3 and 32-bit v1.4 implementations in all but three cases: Ink Scan 

Flat, Digital Live Scan All and Digital Live Scan Rolled. Leading the pack is the highly optimized 32-bit implementation of 

the JasPer CODEC, followed by the 64-bit implementation of the OpenJPEG v.1.4 CODEC.
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4.7. Investigative Goal 7: Comparison of JPEG 2000 Implementation With Respect to 

Decompression Throughput

As with compression throughput, the performance of algorithms can vary for the decompression process by 

implementation (i.e., same algorithm but different vendors or system architecture). The seventh investigative goal of 

this study is to see if such implementation differences do indeed introduce significant operational differences in terms 

of decompression throughput. Once again since the focus of this study is the JPEG 2000 algorithm, this investigative 

goal was limited to lossless JPEG 2000 and does not include RLE, ICER or PNG.

4.7.1. Investigative Analysis 7

The median decompression throughput data in Table 15 shows that there are differences evident between the various 

implementations of JPEG 2000. Pair-wise examination of the data using Wilcoxon Signed Rank Test with a significance 

level of 0.05 also confirmed significant differences for all of these cases but two (OpenJPEG 32-bit v1.3 vs. 32-bit v1.4 

with Ink Card Scan Rolled, and OpenJPEG 32-bit v1.4 vs. JasPer 32-bit with Latent imagery).

Table 15 - Decompression Throughput (Median Time, seconds) by Implementation, Lower is Better

CODEC Image Type

All Data Latent Ink Card Scan Digital Live Scan

All All All Rolled Flat Slap All Rolled Flat Slap

OpenJPEG v.1.3, 32-bit 0.9203 0.9399 0.7877 0.6785 0.3245 3.4319 1.0434 1.0434 0.3958 2.9190

OpenJPEG v.1.3, 64-bit 0.9029 0.9206 0.7954 0.6707 0.3216 3.4006 0.9984 0.9984 0.3893 2.8150

OpenJPEG v.1.4, 32-bit 0.8578 0.8734 0.7174 0.6083 0.2964 3.1198 0.9494 0.9494 0.3482 2.6870

OpenJPEG v.1.4, 64-bit 0.3587 0.3749 0.3086 0.2617 0.1236 1.3258 0.3930 0.3930 0.1435 1.1019

JasPer 1.900.1 32-bit 0.3631 0.3635 0.3302 0.2871 0.1344 1.3726 0.3859 0.3859 0.1566 1.0729

JasPer 1.900.1 64-bit 0.5728 0.5874 0.4994 0.4320 0.2081 2.1527 0.6179 0.6179 0.2404 1.7299

Table 16 - Decompression Throughput, Wilcoxon Signed Rank Test, alpha of 0.05

Case Comparison Pair Image Type

All Data Latent Ink Card Scan Digital Live Scan

CODEC 1 CODEC 2 All All All Rolled Flat Slap All Rolled Flat Slap

1 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.3, 64-bit <0.0001 <0.0001 <0.0001 0.4836 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

2 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

3 OpenJPEG v.1.3, 32-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

5 OpenJPEG v.1.3, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

6 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

7 OpenJPEG v.1.3, 64-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

8 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

9 OpenJPEG v.1.3, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

10 OpenJPEG v.1.4, 32-bit OpenJPEG v.1.4, 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

11 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 32-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

12 OpenJPEG v.1.4, 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

13 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 32-bit <0.0001 0.2684 <0.0001 <0.0001 <0.0001 <0.0001 0.00029 <0.0001 <0.0001 <0.0001

14 OpenJPEG v.1.4, 64-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

15 JasPer 1.900.1 32-bit JasPer 1.900.1 64-bit <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

4.7.2. Investigative Result 7

Examination of various implementations of JPEG 2000 in both 32-bit and 64-bit architectures yielded statistically 

significant differences in decompression throughput in all but two cases. These were OpenJPEG 32-bit (v1.3 and v1.4)

on Ink Card Scan Rolled and Digital Live Scan Rolled, and OpenJPEG 32-bit v1.4 and JasPer 32-bit on Latent. Leading the 

pack is the 64-bit implementation of the OpenJPEG v.1.4 followed by the 32-bit implementation of the JasPer CODEC. It 

should also be noted that for the same algorithm version, the 32-bit compilation of that version provided lower 

decompression throughput than the 64-bit in almost all the cases, except with the JasPer implementation which favors 

the 64-bit platform.
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4.8. Investigative Goal 8: Comparison of Implementation Complexity

Another factor that can impact the selection of an algorithm is the complexity of the code relative to the target 

platform being used. A true complexity analysis requires profiling of the algorithm with instrumented source code 

during typical execution. A rudimentary method of complexity analysis can be an estimation of complexity based on the 

lines of code in the source files that comprise the algorithm implementation. This complexity estimation is anecdotal 

and is not considered a good measure of complexity as a few lines of code may be executed at an exponential 

redundancy while many lines of code may execute in linear fashion. This section has been included nonetheless for 

discussion purposes.

4.8.1. Investigative Analysis 8

The source files of each respective algorithm were processed; the number of lines of code (LOC), the number of 

comments/empty line breaks, and the number of individual source files were all measured. The results of these 

measures are presented below in Table 17.

Table 17 - Implementation Complexity

CODEC Gross Lines of 

Code (LOC)

Lines of 

Comments/Blanks

Net Lines of Code 

(LOC)

Number of 

Source Files

ICER 6883 991 5892 19

RLE used in BMP 39812 1713 38099 42

JasPer 42235 3857 38378 108

OpenJPG 1.3 70076 11601 58475 163

PNG (LIBPNG+ZLIB) 78893 9094 69799 116

OpenJPG 1.4 87676 14257 73419 179

4.8.2. Investigative Result 8

Based on the LOC analysis, the highly optimized ICER CODEC provides the lowest LOC count, and may therefore be the 

least complex. Based on the intended purpose of ICER being run on an autonomous space craft with limited 

computational resources, this finding would agree with that goal as ICER’s codebase appears to be a fraction of even 

the second least complex algorithm, RLE.
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5. Conclusions

This experiment was conducted with eight primary goals:

1. Examine Effective Compression Rates by Algorithm

2. Examine Compression Throughput by Algorithm

3. Examine Decompression Throughput by Algorithm

4. Examine Effective Compression Rate by Impression Type

5. Compare JPEG 2000 Implementation With Respect to Effective Compression Rate

6. Compare JPEG 2000 Implementation With Respect to Compression Throughput

7. Compare JPEG 2000 Implementation With Respect to Decompression Throughput

8. Compare Implementation Complexity

Based on the results, it can be concluded that the wavelet-based algorithms (JPEG 2000 and ICER) provide far better 

effective compression rates than their non-wavelet-based counterparts (PNG and RLE using BMP) on mixed-image data 

consisting of Ink Card Scan, Digital Live Scan, and Latent. Given specific impression types, the PNG algorithm leads all 

others for the case of Digital Live Scan Rolled. It is hypothesized that the lower entropy of this particular image type 

may make these images more effectively compressible by PNG. 

In terms of compression throughput, the non-wavelet-based algorithms lead the pack with the exception of the highly 

optimized JasPer 2000 (32-bit) which provides compression throughputs on par with the non-wavelet-based

algorithms. With decompression however, PNG clearly leads the pack by a large margin. While PNG has many uses, it 

was primarily designed to be a format suitable for the transmission and display of raster images transmitted across 

networks and displayed in web browsers. This is a highly asymmetric use case where images are typically compressed 

once and viewed/decompressed many times. Optimization for such use cases, (compress once, view many times), as 

well as algorithm complexity, have provided PNG this edge in decompression throughput.

In examining algorithm performance with respect to the various image/impression types used in this study, the image 

type used did not appear to have a large impact on changing the rank order dramatically. Where a ranking shift was 

noted, this shift was typically only one rank position (i.e., an algorithm that ranked 6th best for Ink Card Scan Rolled 

imagery may have fared slightly better in the 5th place with another image type for a given measurement criteria). The 

one exception was PNG, which jumped from a 4

th place ranking with All Data in terms of effective compression rate to 

1

st place for Digital Live Scan Rolled. Considering its fast decompression times, the PNG algorithm may be particularly 

well suited for Digital Live Scan Rolled images.

In studying the behavioral differences of various platforms or implementations of a given algorithm, examination of 

various implementations of JPEG 2000 in both 32-bit and 64-bit architectures yielded nearly identical results in terms of 

effective compression rates with some very small differences. While these small differences were statistically significant 

in almost all cases, they are not operationally relevant (for example, where OpenJPEG v.1.3 yields an effective rate of 

1.9550:1 and OpenJPEG v.1.4 yields an effective rate of 1.9551:1). The various architectures did exhibit statistically 

significant differences in throughput but these differences were not consistent enough to make a generalized 

conclusion. For example, in almost all cases, 32-bit OpenJPEG implementation was slower than the 64-bit OpenJPEG 

implementation. For all those cases, the reverse was true with JasPer where the 32-bit version excelled.

Finally, based on a simple comparison of the number of lines of code that comprised each algorithm, the ICER CODEC is 

comprised of the fewest lines of code, and may perhaps be the least complex. The next two algorithms are RLE and 

JasPer.

Based on this study it can be generally concluded (with specific exceptions as noted) that the non-wavelet-based

algorithms provide an edge in throughput while the wavelet-based algorithms provide an edge in effective 

compression rates.
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6. Discussions and Future Work

Throughout this study, most of the selected measures of performance yielded real (floating point) numbers of very 

high precision. Comparison of such numbers in cases of very similar (but different) value may yield differences which 

can be operationally inconsequential, but would nonetheless be statistically significant. In this study, we attempted to 

mitigate this by conducting preliminary mathematical operations at six significant digit precision with the final stages of 

analysis at a precision of four. Such a strategy may only be effective in the scope of image sizes used in this study and 

generalization of it may call for adjustment of the rounding depending on the distribution of expected image sizes. This 

may be a ripe topic for future study where the very metrics of compression parameters can be explored in a framework 

relevant to the systems for which the operations are intended. 
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References

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156/156I.pdf

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Orandi, S. August 19, 2009. http://www.nist.gov/customcf/get_pdf.cfm?pub_id=903078. Retrieved 

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LIKERT Likert, R. (1932). A Technique for the Measurement of Attitudes, Archives of Psychology 140, 55.

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Compression on 1000 ppi Fingerprint Imagery, NIST Interagency Report 7778, National Institutes of 

Standards and Technology, Gaithersburg, MD. April 11, 2011, 72 pages.

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Algorithms on 500 ppi Fingerprint Imagery, NIST Interagency Report 7781, National Institutes of 

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Matching Tenprint Images," NIST Technical Report NISTIR 6534 & CD-ROM, June 2000.

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operational ROC analysis, Journal of Research of the National Institute of Standards and Technology, 

116(1), 517-537, (2011).

WU2 Wu, Jin Chu. Studies of Operational Measurement of ROC Curve on Large Fingerprint Data Sets Using 

Two-Sample Bootstrap. NISTIR 7449, U.S. Department of Commerce, National Institute of Standards 

and Technology, September 2007, 25 pages.

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2008, 23 pages.
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Standards

AN2011 NIST Special Publication 500-290: American National Standard for Information Systems : Data Format 

for the Interchange of Fingerprint, Facial & Other Biometric Information (ANSI/NIST-ITL 1-2011). 

Approved November 2011. 

ISO/IEC ISO/IEC 15948:2004 – Information technology - Computer graphics and image processing -- Portable 

Network Graphics (PNG): Functional specification. 

http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=29581 Retrieved 

2012-01-11.

JPEG "T.81 : Information technology – Digital compression and coding of continuous-tone still images –

Requirements and guidelines". http://www.itu.int/rec/T-REC-T.81. Retrieved 2011-01-12.

JPEG2K "ISO/IEC 15444-1:2004 - Information technology -- JPEG 2000 image coding system: Core coding system". 

http://www.iso.org/iso/iso_catalogue/catalogue_ics/catalogue_detail_ics.htm?csnumber=27687. 

Retrieved 2009-11-01.

WSQ "WSQ Gray-Scale Fingerprint Image Compression Specification" Version 3.1. 

https://www.fbibiospecs.org/docs/WSQ_Gray-scale_Specification_Version_3_1.pdf. Retrieved 2010-01-11.
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Appendix A. Dataset7 Makeup

For the Ink Card Scan portion of the tests, the study utilized fingerprint images based on the Base Demonstration 

Model (BDM) fingerprints utilized in early tests of the FBI IAFIS system, and later used as the basis for the NIST SD-27 

special database [SD27]. This Ink Card Scan data was collected as a result of law enforcement activities and represents 

actual field data with collection dates ranging from 08/18/1973 through 04/12/1994. The original FD-249 fingerprint 

collection cards with these images were retrieved by NIST and rescanned at 1000 ppi by NIST personnel under 

controlled conditions. The images were scanned at 8 bits per pixel gray-scale using FBI certified software (Appendix F 

compliant) and stored in a non-compressed format to ensure no compression anomalies were introduced into the 

original set.

For the Digital Live Scan portion of the tests, the study again utilized actual operational data captured during normal 

enforcement activities with collection dates ranging from 01/04/2010 through 04/13/2010. The Digital Live Scan data was 

stored so as never to have been subject to lossy compression.

Where possible, the image sets were equally balanced by gender, finger, pattern class and hand. It should be noted that 

balancing equally does not follow the natural demographic behavior of the population such as gender (48 % males/52 %

female [CIA]) or pattern class (65 % Loops, 30 % Whorls, 5 % Arches [DOJ]). The goal in having equal distributions was to

avoid the potential statistical bias of very small subsamples. That is, all subsamples were equally important with respect 

to compression irrespective of their relative incidence in the population.

Demographic Make-up of Ink Card Scan Datasets

Ink Card Scan images used in this study consisted of 200 each of Rolled, Flat, and Slap. 

Table 18 - Ink Card Scan Data classification by Impression Type

All Data

Impression Type Males Females Right Left

Flat Single Finger 100 100 96 104

Rolled Single Finger 100 100 96 104

Four Finger Slaps 100 100 100 100

Table 19 - Ink Card Scan Pattern Classification for Single Finger Images by Impression Type

Data From Females (Single Finger) Data From Males (Single Finger)

Pattern Class Flat Rolled Right Left Pattern Class Flat Rolled Right Left

Arch 34 34 30 38 Arch 34 34 34 34

Loop 33 33 32 34 Loop 33 33 32 34

Whorl 33 33 34 32 Whorl 33 33 34 32

Total 100 100 96 104 Total 100 100 100 100

 

7

This dataset was chosen to meet constraints required by the NISTIR 7778, but this does not lessen its applicability to this study.
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Table 20 - Ink Card Scan Pattern Classification for Single Finger Images by Finger (Females)

Data From Females (Single Finger)

Pattern Class

R. Thumb

R. Index

R. Middle

R. Ring

R. Little

L. Thumb

L. Index

L. Middle

L. Ring

L. Little

Total

Arch 4 4 3 4 0 3 5 4 3 4 34

Loop 3 3 4 3 3 4 3 3 4 3 33

Whorl 3 4 3 3 4 3 3 4 3 3 33

Total 10 11 10 10 7 10 11 11 10 10 100

Table 21 - Ink Card Scan Pattern Classification for Single Finger Images by Finger (Males)

Data From Males (Single Finger)

Pattern Class

R. Thumb

R. Index

R. Middle

R. Ring

R. Little

L. Thumb

L. Index

L. Middle

L. Ring

L. Little

Total

Arch 4 3 3 4 3 3 4 3 3 4 34

Loop 3 3 4 3 3 4 3 3 4 3 33

Whorl 3 4 3 3 4 3 3 4 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100

Make-up of the digital live Scan data sets

Digital live Scan images used in this study consisted of 200 Rolled,, 200 Flat, and 200 Four Finger Slap impressions. 

Table 22 – Digital Live Scan Data Classification by Impression Type

All Data Records

Impression Type Males Females Right Left

Flat Single Finger 100 100 100 100

Rolled Single Finger 100 100 100 100

Four Finger Slaps 100 100 96 104
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Table 23 – Digital Live Scan Pattern Classification for Single Finger Images by Impression Type

Data From Females (Single Finger) Data From Males (Single Finger)

Pattern Class Flat Rolled Right Left Pattern Class Flat Rolled Right Left

Arch 33 33 36 30 Arch 33 33 36 30

Loop 34 34 30 38 Loop 34 34 30 38

Whorl 33 33 34 32 Whorl 33 33 34 32

Total 100 100 100 100 Total 100 100 100 100

Table 24 – Digital Live Scan Pattern Classification for Single Finger Images by Finger (Females)

Data From Females (Single Finger)

Pattern Class

R. Thumb

R. Index

R. Middle

R. Ring

R. Little

L. Thumb

L. Index

L. Middle

L. Ring

L. Little

Total

Arch 4 4 4 3 3 3 3 3 3 3 33

Loop 3 3 3 3 3 3 4 4 4 4 34

Whorl 3 3 3 4 4 4 3 3 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100

Table 25 – Digital Live Scan Pattern Classification for Single Finger Images by Finger (Males)

Data From Males (Single Finger)

Pattern Class

R. Thumb

R. Index

R. Middle

R. Ring

R. Little

L. Thumb

L. Index

L. Middle

L. Ring

L. Little

Total

Arch 4 4 4 3 3 3 3 3 3 3 33

Loop 3 3 3 3 3 3 4 4 4 4 34

Whorl 3 3 3 4 4 4 3 3 3 3 33

Total 10 10 10 10 10 10 10 10 10 10 100

Data demographics

The fingerprint images used to compile the datasets as described above were taken from several subjects. The 

balancing of the samples used was based on the uniqueness of a single fingerprint and not individual subjects. As such, 

multiple, yet distinct, fingerprint impressions were taken from some subjects (i.e., some subjects contributed more 

than one finger). 
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Table 26 - Gender Breakdown for Data

Subjects by Gender and Race

Males Females White Black Hispanic Asian

Ink Card Scan Dataset 72 17 38 47 3 1

Digital Live Scan Dataset 60 63 11 51 59 2

Combined Dataset (All) 132 80 49 98 62 3

Table 27 - Age Breakdown for Data

Subjects by Age

Under 18 18-24 25-29 30-34 35-39 40-44 45-49 50+

Ink Card Scan Dataset 5 54 18 3 1 4 3 1

Digital Live Scan Dataset 9 38 23 15 18 8 4 8

Combined Dataset (All) 14 92 41 18 19 12 7 9

Table 28 - Other Metadata: Height and Weight

Subjects by Height and Weight

<5’0”

5’0” – 5’5”

5’6” – 5’11”

6’0”+

<100 lbs

100 – 149 lbs

150 – 199 lbs

200 – 249 lbs

250+ lbs

Ink Card Scan Dataset 0 11 59 19 0 27 48 10 4

Digital Live Scan Dataset 4 45 63 11 1 45 56 17 4

Combined Dataset (All) 4 56 122 30 1 72 104 27 8

Table 29 - Other Metadata: Eye Color

Subjects by Eye Color

Brown Black Blue Green Hazel

Ink Card Scan Dataset 69 2 12 1 5

Digital Live Scan Dataset 111 1 2 6 3

Combined Dataset (All) 180 3 14 7 8
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Table 30 - Image Geometry Data

Impression Type Image Width (Pixels) Image Height (Pixels) Image Size (KB, Non-compressed)

Mean Median Min Max Mean Median Min Max Mean Median Min Max

Ink Card Scan Rolled 1016 1002 652 1718 1166 1165 643 2063 1170.8 1134.4 535.0 2568.6

Ink Card Scan Flat 602 592 444 843 785 801 497 1008 459.6 476.0 269.0 564.1

Ink Card Scan Slap 3192 3192 3045 3334 2009 2013 1744 2082 6264.4 6264.7 5339.3 6597.5

Digital Live Scan

Rolled

1600 1600 1600 1600 1500 1500 1500 1500 2343.8 2343.8 2343.8 2343.8

Digital Live Scan Flat 687 674 515 825 1057 993 672 1500 725.1 652.5 377.9 1171.9

Digital Live Scan Slap 3200 3200 3200 3200 2000 2000 2000 2000 6250.0 6250.0 6250.0 6250.0
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Appendix B. Equipment Used for Study

The equipment utilized for the processing of the image data with the algorithms described was comprised of a single 

PC customized specifically for the purposes of imaging software development, research, and testing. The specifications 

of this PC are as follows:

Model: Dell Precision T7500

CPU: 2x Intel Xeon W5580 @ 3.20 GHz

Memory: 12.0 GB DDR3 (Registered ECC)

Storage: 2x OCZ Vertex 2 240GB SSD (RAID 1), 4x Western Digital 2TB HDD (RAID 0+1) 

Operating System: Microsoft Windows 7 64-bit Ultimate Edition
NISTIR 7779 - An Exploration of the Operational Ramifications of Lossless Compression of 1000 ppi Fingerprint Imagery

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Appendix C. Examination of Entropy 

Lossy compression may balance compressed file size against the discard of image information. That is, a lossy CODEC 

may achieve a desired compression ratio by varying the amount of information discarded, or achieve a specified level of 

fidelity to the original image by altering the size of the output compressed file, i.e., a lower compression ratio. Various 

CODECs provide varying degrees of user control over this balance, but in every case, the complexity of image content 

will set the stage for various optimization strategies to be executed by the compression algorithm in balancing 

information loss against compression ratio.

By contrast, lossless image compression, by definition, must preserve all information contained in the non-compressed 

original. Loss of information is not available to the lossless CODEC as part of its optimization scheme. Accordingly, the 

only option available to the lossless algorithm is to increase the compressed file size (i.e., reduce the compression ratio) 

in proportion to the complexity of the input non-compressed original.

In the present study, it was observed that the BMP-RLE CODEC performed reasonably well with the Digital Live Scan

images, but poorly with the Ink Card Scan images, actually resulting in a compressed file size larger than that of the 

non-compressed original. Suspecting that the RLE effectiveness to be related directly to image entropy differences, we 

measured the entropy of both Ink Card Scan and Digital Live Scan for comparison.

Run-length encoding (RLE) is a very simple form of data compression in which runs of data (sequences in which the 

same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as 

the original run. Hence, images containing large regions of homogeneous gray level may be encoded very efficiently 

using RLE. By contrast, highly textured images containing few runs are less efficiently encoded via RLE.

Entropy (E) provides a measure of the average gray level variability of an image. It is defined as:

2

1

( )log ( )

n

I i i

i

E p x p x



 

(C.1)

where n = number of gray levels in the image, i.e. 256; xi = the value of the i

th gray level; p(xi) = the probability of 

occurrence of the i

th gray level in the image. Thus, for a single channel (8 bit) image having p(xi) = 1/256 for every i, the 

maximum entropy is 8, or 8 bits. Accordingly, an image displaying a relatively flat (uniform) gray level histogram should 

yield an entropy very near the maximum value of 8 bits. An image having less uniform distribution of gray levels, such as 

an image displaying large areas homogeneous in gray level, would be expected to yield a lower entropy measure.

Procedure

Entropy was measured for each of the non-compressed source images used in the present study. This included 1000 

ppi digital Scans acquired from: a standard inked 10-print fingerprint cards, and a Digital Live Scan device employing the 

principal of Frustrated Total Internal Reflection (FTIR).

In addition to entropy as defined in equation (1), we measured the ratio of entropy in the image area identified by a 

segmentation procedure as occupied by the background to that of the fingerprint. Hence, we have the Entropy Ratio 

defined as simply:

ingerprint

background

Ratio

f

E

E

E



(C.2)
NISTIR 7779 - An Exploration of the Operational Ramifications of Lossless Compression of 1000 ppi Fingerprint Imagery

PAGE 52 OF 52

Results

Table 31 exhibits mean values of various entropy measures for Ink Card Scan data and those acquired using an FTIR 

Digital Live Scan device. The table resolves the mean values for each of the fingerprint impression types, rolled, flat, and 

4-finger slap. Also included are measures of the proportion (percentage) of the image area identified via the 

segmentation procedure as containing the fingerprint.

Table 31 – Mean measurements of entropy and fingerprint region of images

CODEC

Entropy Image Entropy 

Fingerprint

Entropy 

Background

Entropy Ratio Prop Area 

Fingerprint

Rolled Ink Card Scan 7.6 7.6 6.4 0.848 85.3

Digital Live Scan 3.2 6.5 0.1 0.019 41.0

Flat Ink Card Scan 7.4 7.4 6.0 0.811 63.7

Digital Live Scan 5.7 6.9 1.6 0.227 66.3

Slap Ink Card Scan 7.3 7.6 6.5 0.855 39.7

Digital Live Scan 4.4 7.2 0.8 0.110 43.7

Entropy of the images tends to be higher for Ink Card Scan images in contrast to that of Digital Live Scan images. 

Entropy of fingerprint regions tends to be slightly lower for Digital Live Scan, but the main contrast is between entropy 

of the background regions, where that of Digital Live Scan is substantially lower than that of the Ink Card Scan.

Examination of the measures of the proportion of image area occupied by the fingerprint shows the greatest difference 

with the rolled impressions. This impression type, thus, is most likely to contain large areas of low-entropy background 

most efficiently compressed using the RLE algorithm. Such regions are more easily compressed by other algorithms as 

well yielding the highest effective compression ratios, i.e., smaller compressed file sizes.