Integrity of fingerprint data is essential to biometric and forensic applications. Accordingly, the FBI's Criminal Justice
Information Services (CJIS) Division has sponsored development of software tools to facilitate quality control functions
relative to maintaining its fingerprint data assets inherent to the Integrated Automated Fingerprint Identification System
(IAFIS) and Next Generation Identification (NGI). This paper provides an introduction of two such tools. The first FBI-sponsored
tool was developed by the National Institute of Standards and Technology (NIST) and examines and detects
the spectral signature of the ridge-flow structure characteristic of friction ridge skin. The Spectral Image
Validation/Verification (SIVV) utility differentiates fingerprints from non-fingerprints, including blank frames or
segmentation failures erroneously included in data; provides a "first look" at image quality; and can identify anomalies
in sample rates of scanned images. The SIVV utility might detect errors in individual 10-print fingerprints inaccurately
segmented from the flat, multi-finger image acquired by one of the automated collection systems increasing in
availability and usage. In such cases, the lost fingerprint can be recovered by re-segmentation from the now compressed
multi-finger image record. The second FBI-sponsored tool, CropCoeff was developed by MITRE and thoroughly tested
via NIST. CropCoeff enables cropping of the replacement single print directly from the compressed data file, thus
avoiding decompression and recompression of images that might degrade fingerprint features necessary for matching.
With the advent of digital cinema, medical imaging, and other applications, the need to properly characterize projection display systems has become increasingly more crucial. Several standards organizations have developed or are presently developing measurement procedures (including ANSI, IEC, ISO, VESA, and SMPTE). The National Institute of Standards and Technology (NIST) has played an important role by evaluating standards and procedures, developing diagnostics, and providing technical and editorial input, especially where unbiased technical expertise is needed to establish credibility and to investigate measurement problems.
The NIST Flat Panel Display Laboratory (FPDL) is operated through the Display Metrology Project (DMP) of the Electronic Information Technology Group in the Electricity Division of the Electronics and Electrical Engineering Laboratory of NIST. The DMP works to develop and refine measurement procedures in support of ongoing electronic display metrology, and applies the results in the development of national and international standards for flat panel display characterization.
In earlier papers, NIST proposed a standard illumination source and optical filter targets with which to assess the state-of-the-art of display measurement. The Display Measurement Assessment Transfer Standard (DMATS) was designed to present the display metrologist with a rectangular array of targets such as color filters, polarizers, and grilles, back-lighted by uniform illumination, to be measured using methods and instruments typically used in display performance measurement. A “round robin” interlaboratory measurement exercise using the “standard” artifact suite would enable a first order assessment of display measurement reproducibility, i.e., measurement variability within the electronic display community. The rectangular array design of the DMATS was anticipated to present stray light and color contamination challenges to facilitate identification of error sources deriving from measurement protocols, laboratory environment, and equipment. However, complications in dealing with heating problems threatened to delay the planned laboratory intercomparison. The Gamut Assessment Standard (GAS) was thus designed as an interim solution to enable the NIST scientists and participating measurement laboratories to begin collecting data. The GAS consists of a 150 mm diameter integrating sphere standard illumination source with a stray light elimination tube (SLET) mounted at the exit port. A dual six-position filter wheel is mounted at the SLET exit port. One wheel holds a series of neutral density filters and a second interchangeable wheel holds various color filters. This paper describes the design and construction of the GAS, its initial performance characterization by NIST, and comparison measurements made at NPL. Possible design changes suggested by the results of the preliminary intercomparison are discussed, as are plans for future interlaboratory comparisons and potential use of the GAS as a transfer standard for laboratory self-certification.
A prototype display measurement assessment transfer standard (DMATS) is being developed by the NIST to assist the display industry in standardizing measurement methods used to quantify and specify the performance of electronic display. Designed as an idealized electronic display, the DMATS illumination source emulates photometric and colorimetric measurement problems commonly encountered in measuring electronic displays. NIST will calibrate DMATS units and distribute them to participating laboratories for measurement. Analysis of initial interlaboratory comparison results will provide a baseline assessment of display measurement uncertainties. Also, diagnostic indicators expected to emerge from the data will be used to assist laboratories in correcting deficiencies or in identifying metrology problem areas for further research, such as measurement techniques tailored to new display technologies. This paper describes the design and construction of a prototype DMATS source and preliminary photometric and colorimetric characterization. Also, this paper compares measurements obtained by several instruments under constant environmental conditions and examines the effects of veiling glare on chromaticity measurements.
KEYWORDS: Video, Data modeling, Quality measurement, Standards development, Statistical analysis, Statistical modeling, Performance modeling, Analog electronics, Video processing, Semantic video
Subjective assessment methods have been used reliably for many years to evaluate video quality. They continue to provide the most reliable assessments compared to objective methods. Some issues that arise with subjective assessment include the cost of conducting the evaluations and the fact that these methods cannot easily be used to monitor video quality in real time. Furthermore, traditional, analog objective methods, while still necessary, are not sufficient to measure the quality of digitally compressed video systems. Thus, there is a need to develop new objective methods utilizing the characteristics of the human visual system. While several new objective methods have been developed, there is to date no internationally standardized method. The Video Quality Experts Group (VQEG) was formed in October 1997 to address video quality issues. The group is composed of experts from various backgrounds and affiliations, including participants from several internationally recognized organizations working in the field of video quality assessment. The majority of participants are active in the International Telecommunications Union (ITU) and VQEG combines the expertise and resources found in several ITU Study Groups to work towards a common goal. The first task undertaken by VQEG was to provide a validation of objective video quality measurement methods leading to Recommendations in both the Telecommunications (ITU-T) and Radiocommunication (ITU-R) sectors of the ITU. To this end, VQEG designed and executed a test program to compare subjective video quality evaluations to the predictions of a number of proposed objective measurement methods for video quality in the bit rate range of 768 kb/s to 50 Mb/s. The results of this test show that there is no objective measurement system that is currently able to replace subjective testing. Depending on the metric used for evaluation, the performance of eight or nine models was found to be statistically equivalent, leading to the conclusion that no single model outperforms the others in all cases. The greatest achievement of this first validation effort is the unique data set assembled to help future development of objective models.
Development of video quality metrics has taken support from experimental vision data mainly at two levels of abstraction. On the one hand are the carefully controlled tests of human visual response to well-defined, controlled visual stimuli, such as the ModelFest study. On the other hand are experiments in which viewers rate the global quality of 'natural' video sequences exhibiting impairments of loosely-controlled composition and amplitude, as in the Video Quality Experts Group study. The IEEE Broadcast Technology Society Subcommittee on Video Compression Measurements has initiated an intermediate level approach to video quality assessment aimed toward developing a scale of video impairment and unit of measure by which to describe video distortion in both perceptual and engineering terms. The proposed IEEE study will attempt to define a scale of video impairment in terms of multiple measurements of the just-noticeable-difference (JND) of compression-induced video impairments. A paired comparison psychophysical method will be used to define a psychometric function of the visual sensitivity to compression-induced video impairments of various amplitudes. In this effort, quality assessment is related directly to visual perception of video impairments rather than to the more 'atomic' visual stimuli as used in many human vision experiments. Yet the experimenter's control over the stimuli is greater than that used in much of contemporary video quality testing.
Mosquito noise is a time dependent video compression impairment in which the high frequency spatial detail in video images having crisp edges is aliased intermittently. A new synthetic test pattern of moving spirals or circles is described which generates mosquito noise (MN) under Motion Pictures Expert Group (MPEG) compression. The spiral pattern is one of several NIST-developed patterns designed to stress specific features of compression based on motion estimation and quantization. The 'Spirals' pattern has several spirals or circles superimposed on a uniform background. The frames are filtered to avoid interline flicker which may be confounded with MN. Motion of the spirals and changing luminance of the background can be included to reduce the correlation between successive frames. Unexpectedly, even a static pattern of spirals can induce mosquito noise due to the stochastic character of the encoder. We consider metrics which are specific to the impairment being measured. For mosquito noise, we examine two separable detectors: each consists of a temporal (frame-to-frame) computation applied to the output of a spatial impairment detector which is applied to each frame. The two spatial detectors are: FLATS, which detects level 8 X 8 pixel image blocks; and the root-mean-square (RMS) applied to the image differences between original and compressed frames. The test patterns are encoded at low bit rates. We examine the measured mosquito noise as a function of the Group-of-Pictures (GOP) pattern in the MPEG-2 encoding and find that the GOP structure defines the periodicities of the MN.
Ann Rohaly, Philip Corriveau, John Libert, Arthur Webster, Vittorio Baroncini, John Beerends, Jean-Louis Blin, Laura Contin, Takahiro Hamada, David Harrison, Andries Hekstra, Jeffrey Lubin, Yukihiro Nishida, Ricardo Nishihara, John Pearson, Antonio Pessoa, Neil Pickford, Alexander Schertz, Massimo Visca, Andrew Watson, Stefan Winkler
The Video Quality Experts Group (VQEG) was formed in October 1997 to address video quality issues. The group is composed of experts from various backgrounds and affiliations, including participants from several internationally recognized organizations working int he field of video quality assessment. The first task undertaken by VQEG was to provide a validation of objective video quality measurement methods leading to recommendations in both the telecommunications and radiocommunication sectors of the International Telecommunications Union. To this end, VQEG designed and executed a test program to compare subjective video quality evaluations to the predictions of a number of proposed objective measurement methods for video quality in the bit rate range of 768 kb/s to 50 Mb/s. The results of this test show that there is no objective measurement system that is currently able to replace subjective testing. Depending on the metric used for evaluation, the performance of eight or nine models was found to be statistically equivalent, leading to the conclusion that no single model outperforms the others in all cases. The greatest achievement of this first validation effort is the unique data set assembled to help future development of objective models.
KEYWORDS: Video, Computer programming, Visualization, Video compression, Data modeling, Visual process modeling, Chromium, Calibration, Video processing, Signal to noise ratio
The investigation examines two methodologies by which to control the impairment level of digital video test materials. Such continuous fine-tuning of video impairments is required for psychophysical measurements of human visual sensitivity to picture impairments induced by MPEG-2 compression. Because the visual sensitivity data will be used to calibrate objective and subjective video quality models and scales, the stimuli must contain realistic representations of actual encoder-induced video impairments. That is, both the visual and objective spatio-temporal response to the stimuli must be similar to the response to impairments induced directly by an encoder. The first method builds a regression model of the Peak Signal-To-Noise Ratio (PSNR) of the output sequence as a function of the bit rate specification used to encode a given video clip. The experiments find that for any source sequence, a polynomial function can be defined by which to predict the encoder bit rate that will yield a sequence having any targeted PSNR level. In a second method, MPEG-2-processed sequences are linearly combined with their unprocessed video sources. Linear regression is used to relate PSNR to the weighting factors used in combining the source and processed sequences. Then the 'synthetically' adjusted impairments are compared to those created via an encoder. Visual comparison is made between corresponding I-, B-, and P-frames of the synthetically generated sequences and those processed by the codec. Also, PSNR comparisons are made between various combinations of source sequence, the MPEG-2 sequence used for mixing, the mixed sequence, and the codec-processed sequence. Both methods are found to support precision adjustment of impairment level adequate for visual threshold measurement. The authors caution that some realism may be lost when using the weighted summation method with highly compression-impaired video.
The present investigation compares performance of two objective video quality metrics in predicting the visual threshold for the detection of blocking impairments associated with MPEG-2 compression. The visibility thresholds for both saturated color and gray-scale targets are measured. The test material consists of image sequences in which either saturated color or gray-scale targets exhibiting blocking are varied in luminance contrast from -44 dB to -5 dB against a constant gray background. Stimulus presentation is by the 'method of limits' under International Telecommunications Union Rec. 500 conditions. Results find the detection of blocking impairments at Michelson contrast levels between -28 dB and -33 dB. This result is consistent with values reported by other investigators for luminance contrast detection thresholds. A small, but statistically significant difference is found between the detection threshold of saturated color patterns versus luma-only images. The results suggest, however, that blocking impairment detections controlled mainly by display luminance. Two objective metrics are applied to gray-scale image sequences, yielding measures of perceptible image blocking for each frame. A relatively simple blocking detector and a more elaborate discrete cosine transform error metric correlate well over the contrast range examined. Also, the two measures correlate highly with measured image contrast. Both objective metrics agree closely with visual threshold measurements, yielding threshold predictions of approximately -29 dB.
A method for determining the viewing aspect of an arbitrary image is presented. The image must be of a shape that has bilateral symmetry. The method involves finding the transformation of the input image to a symmetric representation. This transform can then be used to derive a template corresponding to the particular aspect of the image.
We have developed a computationally rapid method of target motion detection using the spatio-temporal constraint equation [1]. This method requires significantly fewer computations than standard methods based on the same equation, and appears to work well even under poor image conditions, such as low contrast, small object size, camera jitter, poor focus, and minimal in-plane motion. This paper will describe both the standard method and our more rapid approach, and will discuss enhancements added to mitigate the effects of registration error and camera jitter. Results, indicating object motion in infrared video images, will be presented.
The spatio-temporal constraint equation for computation of the optical flow holds only over local spatio-temporal regions where motion is translational with constant velocity or over non- moving background regions where the image velocity is zero. Where the expression is true, it is possible to estimate the image motion vector of local image regions by minimizing the squared error over the local region. The expression is not true in the moving boundaries of moving objects over a stationary background, over regions with multiple moving objects, or over objects not in purely translational motion. Under these conditions, the accurate computation of motion vectors is not possible using this method. However, the error squared term, itself, may be used as a moving target indicator able to segment moving targets from noise and background clutter. This paper proposes and assesses the feasibility of using the error measure to detect moving boundaries in high noise images. We assess the performance of this error-squared measure in localizing object motion in high noise environments for two filtering functions G(x,y): the Gaussian function and the Gabor function.
Traditionally, image motion information is extracted from two successive frames or even a sequence of frames. We show that with an image sensor that simultaneously samples the light intensity and its timederivative, image motion can be extracted from a single expanded frame. Image motion sensing can be performed by a set of Gabor filters and their gradient filters. A computational test on some synthetic data shows that the computation of instantaneous motion from one expanded frame is more accurate than from successive images.
Fusion of multiple sensor imagery is an effective approach to clutter rejection in target detection and recognition. However, image registration at the pixel level and even at the feature level poses significant problems. We are developing a neural network computational schemes that will permit fusion of multiple sensor information according to target motion characteristics. One such scheme implements the Law of Common Fate to differentiate moving targets from dynamic background clutter on the basis of homogeneous velocity; spatiotemporal frequency analysis is applied to time-varying sensor imagery to detect and locate individual moving objects. Another computational scheme applies Gabor filters and differential Gabor filters to calculate image flow and then employs a Lie group-based neural network to interpret the 2D image flow in terms of 3D motion, and to delineate regions of homogeneous 3D motion; the motion-keyed regions may be correlated among sensor types to associate multiattribute information with the individual targets in the scene and to exclude clutter.
Gabor-type motion sensors can be used to extract the spatio-teniporal
frequencies of moving image patches. We propose a energy minimization scheme
for the computation of optical flow from spatio-temporal frequencies. This
computational scheme can be implemented by an optical neural system to perform
rea1time visual motion analysis.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.