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PubMed2001

Computational and performance aspects of PCA-based face-recognition algorithms.

Algorithms based on principal component analysis (PCA) form the basis of numerous studies in the psychological and algorithmic face-recognition literature. PCA is a statistical technique and its incorporation into a face-recognition algorithm requires numerous design decisions. We explicitly state the design decisions by introducing a generic modular PCA-algorithm. This allows us to investigate these decisions, including those not documented in the literature. We experimented with different implementations of each module, and evaluated the different implementations using the September 1996 FERET evaluation protocol (the de facto standard for evaluating face-recognition algorithms). We experimented with (i) changing the illumination normalization procedure; (ii) studying effects on algorithm performance of compressing images with JPEG and wavelet compression algorithms; (iii) varying the number of eigenvectors in the representation; and (iv) changing the similarity measure in the classification process. We performed two experiments. In the first experiment, we obtained performance results on the standard September 1996 FERET large-gallery image sets. In the second experiment, we examined the variability in algorithm performance on different sets of facial images. The study was performed on 100 randomly generated image sets (galleries) of the same size. Our two most significant results are (i) changing the similarity measure produced the greatest change in performance, and (ii) that difference in performance of +/- 10% is needed to distinguish between algorithms.

Algorithms↗
PubMed1991

Theoretical and experimental rate distortion performance in compression of ambulatory ECG's.

We compare ECG data compression algorithms based on signal entropy for a given mean-square-error (MSE) compression distortion. By defining the distortion in terms of the MSE and assuming the ECG signal to be a Gaussian process we are able to estimate theoretical rate distortion bounds from average ECG power spectra. These rate distortion bounds give estimates of the minimum bits per second (bps) required for storage of ECG data with a given MSE regardless of compression method. From average power spectra of the MIT/BIH arrhythmia database we have estimated rate distortion bounds for ambulatory ECG data, both before and after average beat subtraction. These rate distortion estimates indicate that, regardless of distortion, average beat subtraction reduces the theoretical minimum data rate required for ECG storage by approximately 100 bits per second (bps). Our estimates also indicate that practical ambulatory recording requires a compression distortion on the order of 11 microV rms. We have compared the performance of common ECG compression algorithms on data from the MIT/BIH database. We sampled and quantized the data to give distortion levels of 2, 5, 8, 11, and 14 microV rms. These results indicate that, when sample rates and quantization levels are chosen for optimal rate distortion performance, minimum data rates can be achieved by average beat subtraction followed by first differencing of the residual signal. Achievable data rates approximate our theoretical estimates at low distortion levels and are within 60 bps at higher distortion levels.

Algorithms↗
PubMed2006

Compression of fluorescence microscopy images based on the signal-to-noise estimation.

Modern microscopic techniques like high-content screening (HCS), high-throughput screening, 4D imaging, and multispectral imaging may involve collection of thousands of images per experiment. Efficient image-compression techniques are indispensable to manage these vast amounts of data. This goal is frequently achieved using lossy compression algorithms such as JPEG and JPEG2000. However, these algorithms are optimized to preserve visual quality but not necessarily the integrity of the scientific data, which are often analyzed in an automated manner. Here, we propose three observer-independent compression algorithms, designed to preserve information contained in the images. These algorithms were constructed using signal-to-noise ratio (SNR) computed from a single image as a quality measure to establish which image components may be discarded. The compression efficiency was measured as a function of image brightness and SNR. The alterations introduced by compression in biological images were estimated using brightness histograms (earth's mover distance (EMD) algorithm) and textures (Haralick parameters). Furthermore, a microscope test pattern was used to assess the effect of compression on the effective resolution of microscope images.

Algorithms↗
PubMed2006

Octree indexing of DICOM images for voxel number reduction and improvement of Monte Carlo simulation computing efficiency.

The purpose of the present study is to introduce a compression algorithm for the CT (computed tomography) data used in Monte Carlo simulations. Performing simulations on the CT data implies large computational costs as well as large memory requirements since the number of voxels in such data reaches typically into hundreds of millions voxels. CT data, however, contain homogeneous regions which could be regrouped to form larger voxels without affecting the simulation's accuracy. Based on this property we propose a compression algorithm based on octrees: in homogeneous regions the algorithm replaces groups of voxels with a smaller number of larger voxels. This reduces the number of voxels while keeping the critical high-density gradient area. Results obtained using the present algorithm on both phantom and clinical data show that compression rates up to 75% are possible without losing the dosimetric accuracy of the simulation.

Algorithms↗
PubMed1994

[The compression of numerical radiological images].

A digital radiological image is made by a number of pixels, each of them characterized by a definite numerical value obtained by quantization which represents the luminance in that specific image unit. Spatial resolution and dynamic range are the main factors in determining the quality of a digital radiological image. The product of the two above factors defines the global dimensions of the image and is expressed in bits. In conventional radiographic images the global dimension of the image is expressed in MBytes, because of its high spatial and contrast resolution. To reduce the visualization and storage requirements as well as the transmission time of large image data sets, compression algorithms have been recently introduced. These algorithms are based on the fact that often in a digital image parts of the binary data are "redundant", that is they are not necessary for correct image representation. Therefore, compression methods are aimed at reducing both statistical and perceptive redundancy. Statistical redundancy is reduced by means of lossless coding which does not allow to compress images with a ratio higher than 4-5:1 and that--by definition--allows to recover the original image quality. On the other hand "lossy" compression algorithms, which eliminate the perceptive redundancy, are based on the reduction of spatial resolution and dynamic range and on transform-based methods. In particular, the latter have usually been more successful in terms of efficient compression, even when applied to conventional radiographic images. The basic transform procedure can be modified at various levels. JPEG is one of these methods, originally developed for photographic images, which can be usefully applied to radiological images as well. Lossy procedures allow to reach higher compression ratios than lossless methods, but the decrease in information content must be prevented from reducing diagnostic accuracy. In order to assess the diagnostic efficiency of the images compressed with lossy methods, semi-objective analyses are usually performed and ROC curves are produced and evaluated. A model ROC analysis is presented.

Algorithms↗
PubMed2006

Rate-distortion analysis of motion-compensated rate scalable video.

Generally speaking, rate scalable video systems today are evaluated operationally, meaning that the algorithm is implemented and the rate-distortion performance is evaluated for an example set of inputs. However, in these cases it is difficult to separate the artifacts caused by the compression algorithm and data set with general trends associated with scalability. In this paper, we derive and evaluate theoretical rate-distortion performance bounds for both layered and continuously rate scalable video compression algorithms which use a single motion-compensated prediction (MCP) loop. These bounds are derived using rate-distortion theory based on an optimum mean-square error (MSE) quantizer, and are thus applicable to all methods of intraframe encoding which use MSE as a distortion measure. By specifying translatory motion and using an approximation of the predicted error frame power spectral density, it is possible to derive parametric versions of the rate-distortion functions which are based solely on the input power spectral density and the accuracy of the motion-compensated prediction. The theory is applicable to systems which allow prediction drift, such as the data-partitioning and SNR-scalability schemes in MPEG-2, as well as those with zero prediction drift such as fine granularity scalability MPEG-4. For systems which allow prediction drift we show that optimum motion compensation is a sufficient condition for stability of the decoding system.

Algorithms↗
PubMed1998

Optimal image resolution for digital storage of radiotherapy-planning images.

PURPOSE: To evaluate the quality of digitized radiation-planning images at different resolution and to determine the optimal resolution for digital storage. METHODS AND MATERIALS: Twenty-five planning films were scanned and digitized using a film scanner at a resolution of 72 dots per inch (dpi) with 8-bit depth. The resolution of scanned images was reduced to 48, 36, 24, and 18 dpi using computer software. Image qualities of these five images (72, 48, 36, 24, and 18 dpi) were evaluated and given scores (4 = excellent; 3 = good; 2 = fair; and 1 = poor) by three radiation oncologists. An image data compression algorithm by the Joint Photographic Experts Group (JPEG) (not reversible and some information will be lost) was also evaluated. RESULTS: The scores of digitized images with 72, 48, 36, 24, and 17 dpi resolution were 3.8 +/- 0.3, 3.5 +/- 0.3, 3.3 +/- 0.5, 2.7 +/- 0.5, and 1.6 +/- 0.3, respectively. The quality of 36-dpi images were definitely worse compared to 72-dpi images, but were good enough as planning films. Digitized planning images with 72- and 36-dpi resolution requires about 800 and 200 KBytes, respectively. The JPEG compression algorithm produces little degradation in 36-dpi images at compression ratios of 5:1. CONCLUSION: The quality of digitized images with 36-dpi resolution was good enough as radiation-planning images and required 200 KBytes/image.

Humans↗
PubMed2002

Effects of high-frequency emphasis and compression time constants on speech intelligibility in noise.

The objectives of the study were to evaluate the effect of different settings with regard to speech intelligibility in noise both objectively and subjectively and thus determine a favoured setting of compression time parameters, pre-set program (high-frequency emphasis) or combination for each individual user in a prospective study. Another objective was to evaluate the relationship between patient characteristics (e.g. slope of hearing loss) and favoured settings. In total, 38 subjects divided over five audiological centres were fitted with the Philips Spaceline D71-40 BTE digital hearing aid. Subjects were asked to compare three predefined compression algorithms with different time constants, slow (indicated by the manufacturer as AVC), intermediate (NORMAL) and fast (SYLLABIC) over two 4-week periods using the intermediate setting in both comparisons and randomizing over the fast and slow conditions. A randomization determined whether a subject started with the comfort-oriented pre-set program (AUTO) or the speech intelligibility-oriented setting with high-frequency emphasis (SPIN). In a third 4-week period, the pre-sets AUTO and SPIN were compared using the setting of the compression time constants that gave the best results during the first two periods. Comparisons were made using a standard speech-in-noise test with three types of noise: continuous speaker noise, modulated ICRA-4 noise, and car noise. The patients were also asked to fill in a Dutch translation and adaptation of the APHAB questionnaire to indicate their impression of performance. The results indicate that no compression algorithm, pre-set or combination is favoured overall. The largest improvement in speech-in-noise scores was found with syllabic compression. The advantageous effect of high-frequency emphasis after optimization of compression timing is small. The APHAB showed that users tend to prefer the SPIN setting. We found no relationship between favoured compression or pre-set and the degree or steepness of the hearing loss User preference and best performance in noise do not always coincide. Acclimatization may play a role. It could be advisable to let a user listen to a subjectively less favoured condition for at least some time if an optimal setting for speech intelligibility in noise is preferred over comfort.

Acoustic Stimulation↗
PubMed2000

Information content of protein sequences.

The complexity of large sets of non-redundant protein sequences is measured. This is done by estimating the Shannon entropy as well as applying compression algorithms to estimate the algorithmic complexity. The estimators are also applied to randomly generated surrogates of the protein data. Our results show that proteins are fairly close to random sequences. The entropy reduction due to correlations is only about 1%. However, precise estimations of the entropy of the source are not possible due to finite sample effects. Compression algorithms also indicate that the redundancy is in the order of 1%. These results confirm the idea that protein sequences can be regarded as slightly edited random strings. We discuss secondary structure and low-complexity regions as causes of the redundancy observed. The findings are related to numerical and biochemical experiments with random polypeptides.

Algorithms↗
PubMed2002

Cascaded differential and wavelet compression of chromosome images.

This paper proposes a new method for chromosome image compression based on an important characteristic of these images: the regions of interest (ROIs) to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless compression of chromosome ROIs with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome ROIs for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. The well-known set partitioning in hierarchical trees (SPIHT) (Said and Perlman, 1996) algorithm is modified to generate separate embedded bit streams for both chromosome ROIs and the rest of the image that allow continuous lossy-to-lossless compression of both (although lossless compression of the former is commonly used in practice). Experiments on two sets of sample chromosome spread and karyotype images indicate that the proposed approach significantly outperforms current compression techniques used in commercial karyotyping systems and JPEG-2000 compression, which does not provide the desirable support for lossless compression of arbitrary ROIs.

Algorithms↗
PubMed1992

ECG data compression by corner detection.

An ECG sampled at a rate of 360, 500 samples s-1 or more produces a large amount of redundant data that are difficult to store and transmit. A process is therefore required to represent the signals with clinically acceptable fidelity and with the least code bits possible. In the paper, a real-time ECG data compressing algorithm, CORNER, is presented. CORNER is an efficient algorithm which locates significant samples and at the same time encodes the linear segments between them using linear interpolation. The samples selected include, but are not limited to, the samples that are significantly displaced from the encoded signal such that the allowed maximum error is limited to a constant epsilon which is specified by the users. The way in which CORNER computes the displacement of a sample from the encoded signal guarantees that the high activity regions are more accurately coded. The results are compared with those of the well known data compression algorithm, AZTEC, which is also a real-time algorithm. It is found that, under the same bit rate, a considerable improvement of the signal-to-noise ratio (SNR) and root mean square error (RMSerr) can be achieved by employing the proposed CORNER algorithm. An average value of SNR (RMSerr) of 27.0 dB (5.668) can be achieved even at an average bit rate of 0.79 bit sample-1 by employing CORNER, whereas the average value of SNR (RMSerr) achieved by AZTEC under the same bit rate is 16.60 dB (19.368).

Algorithms↗
PubMed1993

AZTDIS--a two-phase real-time ECG data compressor.

An ECG sampled at a rate of 360 samples s-1 or more produces a large amount of redundant data that are difficult to store and transmit; we therefore need a process to represent the signals with clinically acceptable fidelity and with as small a number of code bits as possible. In this paper, a real-time ECG data-compression algorithm, AZTDIS, is presented. AZTDIS is an efficient algorithm which locates significant samples and at the same time encodes linear segments between them by using linear interpolation. The significant samples selected include, but are not limited to, the samples that have significant displacement from the encoded signal such that the allowed maximal error is limited to a constant epsilon, which is specified by the user. The way that AZTDIS computes the displacement of a sample from the encoded signal guarantees that the high activity regions are more accurately coded. The results from AZTDIS are compared with those from the well-known data-compression algorithm, AZTEC, which is also a real-time algorithm. It is found that under the same bit rate, a considerable improvement of root-mean-square error (RMSerr) can be achieved by employing the proposed AZTDIS algorithm. An average value of RMSerr of 9.715 can be achieved even at an average bit rate of 0.543 bits per sample by employing AZTDIS. By tuning the allowed maximal error of AZTDIS such that it has similar bit rate to AZTEC, the average value of RMSerr achieved by AZTDIS is 5.554 while the average value of RMSerr achieved by AZTEC under the same bit rate is 19.368.

Algorithms↗
PubMed2004

Wavelet-based ultra-high compression of multidimensional NMR data sets.

The application of a lossy data compression algorithm based on wavelet transform to 2D NMR spectra is presented. We show that this algorithm affords rapid and extreme compression ratios (e.g., 800:1), providing high quality reconstructed 2D spectra. The algorithm was evaluated to ensure that qualitative and quantitative information are retained in the compressed NMR spectra. Whilst the maximum compression ratio that can be achieved depends on the number of signals and on the difference between the most and the least intense peaks (dynamic range), a compression ratio of 80:1 is affordable even for the challenging case of homonuclear 2D experiments of large biomolecules.

Algorithms↗
PubMed2000

ECG data compression using optimal non-orthogonal wavelet transform.

This paper introduces an effective technique for the compression of electrocardiogram (ECG) signals. The technique is based on a new class of non-orthogonal discrete wavelet transform (DWT). The performance of ECG compression algorithm is measured by its ability to minimize distortion while retaining all clinically significant features of the signal. The percent root-mean square difference (PRD) is used as an accepted standard for measuring the signal distortion. However, there is no standard for measuring the clinically significant features retained after signal reconstruction. The coefficients of the DWT are calculated such that the square of the difference between the original signal and the reconstructed one is minimum in least mean square sense. The resulting transforms deal with signals of arbitrary lengths; that means the signal length is not restricted to be a multiple of power of 2. Numerical results comparing the performance of the constructed non-orthogonal transform with that of W-transform and Daubechies D(4) orthogonal transform are given. These results show that, independent of signal length, the decomposition of the signal up to the fourth level is sufficient for getting minimum PRD. In addition, the proposed technique yields the lowest PRD compared to the other two algorithms and for a compression ratio less than 10 the optimal transform can be obtained for only one ECG period. However, for a higher compression ratio the PRD is smaller for long signals.

Algorithms↗
PubMed2004

Is JPEG compression of videomicroscopic images compatible with telediagnosis? Comparison between diagnostic performance and pattern recognition on uncompressed TIFF images and JPEG compressed ones.

Early melanoma diagnosis is an important goal for dermatologists. Polarized light systems are increasingly employed for dermatoscopic diagnosis of melanocytic lesions. For the purpose of teledermoscopy, whose importance is increasingly growing for consultation and teaching purposes, it is of utmost importance to establish whether, after compression, polarized light images maintain their informativeness. The aim of our study was to check the effects of compression on melanocytic lesion images acquired by means of a digital videomicroscope on the identification of morphological details of the image and on diagnostic accuracy. A total of 170 50-fold-magnified pigmented skin lesion images, acquired in Tagged Image File Format (TIFF) by a digital videomicroscope, were compressed using Joint Photographic Experts Group (JPEG) algorithms (compression factor 30). Two experts in videomicroscopy evaluated both original and compressed images twice by describing single lesion features and expressing a diagnosis. Reproducibility in the assessment of dermoscopic parameters and observer performance were studied by kappa statistics and Receiver Operating Characteristic (ROC) analysis. Both intra- and interobserver reproducibility in the assessment of morphological details were higher when TIFF images were considered, indicating a better image quality. Nonetheless, there was no significant difference in the diagnostic accuracy between uncompressed images and compressed ones, although the intraobserver reproducibility in the diagnostic judgement was higher for uncompressed images. Despite loss in image details, factor 30 compressed videomicroscopic images enable a good diagnostic accuracy.

Algorithms↗
PubMed1989

Full-frame transform compression of CT and MR images.

Compression algorithms based on full-frame discrete cosine transforms have achieved compression ratios as high as 10:1 to 20:1 with almost imperceptible image degradation, when applied to projection radiographs digitized with 2,048 X 2,048 X 8-bit matrices. Compared with such radiographs, images obtained with computed tomography (CT) and magnetic resonance (MR) are smaller in size, have lower signal-to-noise ratios, and, in the case of CT, have a larger dynamic range. These differences result in qualitatively different spectral properties. The authors studied the efficiency of the full-frame technique when applied to CT and MR images. They achieved excellent results, with compression ratios in the neighborhood of 5:1. The study was performed with the use of a hardware implementation of the authors' algorithm, which can compress a 512 X 512 X 12-bit image in less than 1.5 seconds.

Algorithms↗
PubMed2001

A multistage perceptual quality assessment for compressed digital angiogram images.

This paper describes a multistage perceptual quality assessment (MPQA) model for compressed images. The motivation for the development of a perceptual quality assessment is to measure (in)visible differences between original and processed images. The MPQA produces visible distortion maps and quantitative error measures informed by considerations of the human visual system (HVS). Original and decompressed images are decomposed into different spatial frequency bands and orientations modeling the human cortex. Contrast errors are calculated for each frequency and orientation, and masked as a function of contrast sensitivity and background uncertainty. Spatially masked contrast error measurements are then made across frequency bands and orientations to produce a single perceptual distortion visibility map (PDVM). A perceptual quality rating (PQR) is calculated from the PDVM and transformed into a one to five scale, PQR(1-5), for direct comparison with the mean opinion score, generally used in subjective ratings. The proposed MPQA model is based on existing perceptual quality assessment models, while it is differentiated by the inclusion of contrast masking as a function of background uncertainty. A pilot study of clinical experiments on wavelet-compressed digital angiogram has been performed on a sample set of angiogram images to identify diagnostically acceptable reconstruction. Our results show that the PQR(1-5) of diagnostically acceptable lossy image reconstructions have better agreement with cardiologists' responses than objective error measurement methods, such as peak signal-to-noise ratio A Perceptual thresholding and CSF-based Uniform quantization (PCU) method is also proposed using the vision models presented in this paper. The vision models are implemented in the thresholding and quantization stages of a compression algorithm and shown to produce improved compression ratio performance with less visible distortion than that of the embedded zerotrees wavelet (EZWs).

Algorithms↗
PubMed1990

Application of region of interest definition to quadtree-based compression of CT images.

A quadtree-based data compression algorithm can provide different levels of compression within and outside of regions of interest (ROIs). The current study shows whether ROI compression can provide greater compression or diagnostic accuracy than uniform quadtree compression. In 75 single CT images from 75 consecutive abdominal examinations, 43 abnormalities were identified and surrounded by ROIs. Three radiologists interpreted the images following (1) 50:1 compression of the entire image; (2) ROI compression at five decreasing compression ratios (with 50:1 compression outside the ROI); and (3) reversible (lossless) compression of the entire image. Reversible compression (compression ratio 3:1) yielded a sensitivity of 96%. ROI compression of 15:1 was achieved with no loss of sensitivity; ROI compression of 28:1 yielded a sensitivity of 91% (not significantly different). At any given compression ratio, diagnostic sensitivity was greater with ROI compression than with uniform quadtree compression. For purposes of image archiving, quadtree-based ROI compression is superior to uniform compression of CT images.

Algorithms↗