Objective Quality Measures Research Articles

Using multiple frequency bins for stabilization of FD-ICA algorithms

In the frequency domain independent component analysis approaches for audio sources separation, the convolutive mixing problem is replaced by the solution of several instantaneous mixing problems, one for each frequency bin of the short time Fourier transform. This methodology yields good results but requires the solution of the permutation ambiguity. Moreover, the performance of the separation algorithms for each bin is not guaranteed to be equivalent, thus some bins can have worse results than others. In this paper a technique based on data from multiple bins is proposed to address these issues. The use of multiple bin information produces a coupling of the separation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing the computational cost. This can be mitigated by a sub-sampling of the multiple bins information. The results show that both approaches are beneficial for the frequency domain ICA approach, producing better separation in terms of objective quality measures.

Improving Graph-Based Image Segmentation Using Nonlinear Color Similarity Metrics

We present a new segmentation method called weighted Felzenszwalb and Huttenlocher (WFH), an improved version of the well-known graph-based segmentation method, Felzenszwalb and Huttenlocher (FH). Our algorithm uses a nonlinear discrimination function based on polynomial Mahalanobis Distance (PMD) as the color similarity metric. Two empirical validation experiments were performed using as a golden standard ground truths (GTs) from a publicly available source, the Berkeley dataset, and an objective segmentation quality measure, the Rand dissimilarity index. In the first experiment the results were compared against the original FH method. In the second, WFH was compared against several well-known segmentation methods. In both cases, WFH presented significant better similarity results when compared with the golden standard and segmentation results presented a reduction of over-segmented regions.

Objective measures of perceptual quality for predicting speech intelligibility in sensorineural hearing loss.

An objective method to predict speech intelligibility in sensorineural hearing loss of different types and increasing degrees of severity is proposed and validated with experimental data. The novel approach is based on the combined use of acoustic simulations of impaired perception and objective measures of perceptual speech quality (PESQ). Acoustic simulations were obtained after degradation of the original, non distorted, speech waveforms by spectral smearing, expansive nonlinearity, and level scaling. PESQ was used to measure perceptual quality of the acoustic simulations obtained by varying the degree of the simulated hearing loss. A logistic function was applied to transform PESQ scores into predicted intelligibility scores. A set of CV and VC syllables in /a/, /u/, and /i/ contexts was used as reference test material. The method was validated with subjective measures of intelligibility of the degraded speech obtained in a group of 10 normal hearing subjects. Overall, prediction of experimental speech intelligibility through the transformed PESQ measures was good (R(2)=0.7; RMSE=0.08) revealing that the proposed approach could be a valuable aid in real clinical applications.

Efficient naturalness restoration for non‐uniform illumination images

This study presents a novel image-naturalness restoration for the purpose of achieving better image quality with respect to the human visual system. Specifically, the authors suppress the artefacts around edges using image gradient components. They also appropriately enhance the image brightness using an adaptive gamma correction. In particular, they simply create a mapping curve to adjust the contrast, and then perform contrast enhancement. Experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in terms of subjective and objective quality measures, and even computational speed. Therefore they believe that the proposed approach can be a useful tool to improve the visual quality of images under non-uniform illumination.

Cushing's syndrome is associated with sleep alterations detected by wrist actigraphy.

The association between the hypothalamic-pituitary-adrenal (HPA) axis and sleep is well described. It is also known that HPA axis disturbances have an effect on sleep. In fact, patients affected by Cushing's syndrome (CS) often complain about poor sleep quality. Our aim was to evaluate objective sleep quality and duration in patients with Cushing's syndrome in active phase, using wrist actigraphy. In 12 patients with active CS without ongoing specific therapy (11F, 1M; age 40.0±10.9years; BMI 28.4±6.7kg/m(2)) and 12 healthy control subjects (HS) (11F, 1M; age 44.0±11.0years; BMI 23.9±4.2kg/m(2)) an actigraphic evaluation was performed on 3 consecutive days under free living conditions. Objective measurement of sleep duration and quality was estimated by an actiwatch, which is a wristwatch-like device used to detect motor activity. In CS patients, wrist actigraphy showed higher fragmented sleep (fragmentation index CS 16.2±4.2, HS 13.0±3.6; p=0.034) and increased nocturnal motor activity (total activity score CS 8318±4308, HS 4971±2372; p=0.020; mean activity score CS 8.7±4.2, HS 5.4±2.2; p=0.030; mean score in active time CS 104.8±39.2, HS 74.8±23.1; p=0.030). On the contrary, actual sleep time resulted similar in CS and HS. No correlation was found between sleep alterations and urinary free cortisol in patients. The impaired actigraphic parameters described in our study suggest that hypercortisolism is associated with sleep alterations, which could contribute to the worsening of life quality and metabolic comorbidities associated with CS. These results have to be confirmed in a larger cohort of patients, using more accurate instruments for sleep assessment.

Comparative Analysis of Image Enhancement Technique for Hyperspectral Palmprint Images

Enhancement is one of the most important and difficult techniques, also the first stage in the pre-processing of images which have to be subjected to image recognition algorithms. The goal of image enhancement is to improve the quality of images. Palmprint image quality is an important factor in the performance of hyperspectral palmprint recognition system. To the best of knowledge there is no evidence of work specifically directed towards image enhancement techniques on hyperspectral palm print images. In this paper different thirteen types of image enhancement techniques are compared based on image quality measure (subjective and objective), subjective image quality measure based on histogram and objective quality measure is based on mean square error (MSE), Peak signal to noise ratio (PSNR), Normalized cross correlation (NK), Average difference (AD), Structural content (SC), Maximum difference (MD), Normalized absolute error (NAE). Such a comparison would be useful in determining the best suited image enhancement method for hyperspectral palmprint. Median filter gives good performance as compare to other image enhancement techniques. The performance of different thirteen image enhancement techniques are tested on PolyU hyperspectral palmprint database. The comparative results are tabled.

Performance Study of Novel Median Filter for Image De-Noising

Most common method of clearing image noise is through median filtration. Median smoothers discard outliers (or impulses) quite effectively at lower noise densities but, fail to provide adequate smoothing for heavily noised images for the simple reason that, in the conventional median filter approaches, each and every pixel of an image is filtered without concern about healthy pixels. To suppress this deficiency, in this paper, an improved median filter by the name Novel Median Filter (NMF) is proposed to enhance the effectiveness of the median filter at higher noise situations to provide a detail preserving de-noising of digital images. Proposed NMF algorithm is a 3-stage filter; first two stages are dedicated to selective switching median filtration in which noisy pixels are first detected and then the filtering operation is applied to restore their values with a valid (or a noise free) median computed for each 3×3 window position. Third level processing enables to remove any distortions present in the de-noised image. The proposed method, Novel Median Filter (NMF) is evaluated for its effectiveness in detail preserving de-noising capability on a large number of digital grey and color images affected with impulsive and non-impulsive noise in terms of objective and subjective quality measures. To better appraise the efficacy of the proposed method, Novel Median Filter (NMF), MATLAB simulation results in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and the de-noised images are obtained and are compared with those obtained for a standard median filter (SMF) and its few variants and meaningful conclusions are derived. Detail preservation capability of the proposed filter has been demonstrated through experimentation on medical samples. General Terms Image Processing, Nonlinear Filter, Median Filtration.

P94. Effects of non-invasive ventilation on sleep and nocturnal ventilation in patients with amyotrophic lateral sclerosis

The majority of patients with amyotrophic lateral sclerosis (ALS) develop chronic ventilatory failure which substantially adds to disease burden and massively affects prognosis. Both quality of life and life expectancy can be influenced by non-invasive ventilation (NIV). Initiation of NIV is indicated in case of sleep-disordered breathing (SDB), daytime hypercapnia, or significant diaphragmatic weakness. Since nocturnal hypercapnia and sleep apnea substantially reduce sleep quality we investigated the effects of NIV on objective measures of both sleep quality and ventilation in patients with ALS. We retrospectively analysed polysomnography (PSG) and oxycapnometry (OC) data obtained from patients with ALS in whom NIV was initiated based on the presence of SDB. PSG and OC were used in all patients for establishing the diagnosis of SDB (t0), for treatment initiation (t1) and for follow-up examination at 3, 9, and 15 months (t2, t3, and t4, respectively). As outcome parameters we chose sleep efficiency (SE), slow wave sleep percentage (N3), REM sleep percentage (REM), apnea hypopnea index (AHI), oxygen desaturation index (ODI), desaturation time (T < 90), respiratory rate (RR) and maximal nocturnal transcutaneous carbon dioxide tension (tc-CO2). Records from 62 patients were analysed. Mean age was 63.2 years, and mean disease duration was 23.1 months. 28 patients were female, and 22 patients reported bulbar onset of ALS symptoms. All patients were started on pressure and volume-controlled bilevel ventilation via a nasal or oronasal interface. Comparison of the above parameters at t0 and t1 showed significant improvement of AHI and ODI, oxygenation, CO2 elimination, and sleep stage duration (N3, REM) in the first night on NIV. Sleep efficiency was neither improved nor reduced by NIV. At t2, t3 and t4 the number of PSG records available became smaller because in many patients, follow-up studies included rather polygraphy than PSG (t2, 33 patients, t3, 15 patients, t4, 11 patients with PSG data). ANOVA for repeated measures revealed stable normoxia and eucapnia under bilevel ventilatory support. Objective measures of sleep quality did not show any significant change over time. Increasing duration of NIV per day was found in all patients in the course of the disease, reflecting disease progression. In patients with ALS, NIV initiation significantly improves objective sleep quality, oxygenation and CO2 elimination in the first night of treatment. Over time, NIV is able to warrant nocturnal normoventilation with only minor changes to ventilator settings. Objective sleep quality does not significantly change in the first 15 months after onset of NIV.

Objective measurements of image quality in synchrotron radiation phase-contrast imaging versus digital mammography.

Phase-contrast mammography with synchrotron radiation is an innovative X-ray imaging practice that improves the identification of breast lesions. Previous studies have proven the superiority of the mammography images taken in the phase-contrast modality using synchrotron radiation beams as compared with images taken in conventional mammography by subjective analyses. However, to our knowledge, no previous study has compared different acquisition systems in order to quantify this improvement by means of objective robust indicators. In this research, we intend to quantify the superiority of phase-contrast imaging by means of objective metrics of image quality. Images from the American College of Radiology Mammographic Accreditation Phantom were obtained at hospitals, in two digital mammography equipment and at the Elettra synchrotron radiation facility (Trieste, Italy), using free space propagation phase-contrast modality. Regions of interest were selected to analyze image quality at the fibers (phase object) and masses (area object) simulated on the phantom by means of the signal-to-noise ratio, the figure of merit, the contrast and the edge visibility. The image contrast and edge visibility were significantly higher at the phase-contrast modality as compared with digital mammography equipment. The figure of merit using phase-contrast modality was higher for the fibers and comparable for the masses. The results showed an improvement of the contrast and edge visibility in phase-contrast images. These improvements may be important in the detection of small lesions and details.

CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.

Large-scale recovery of genomes from isolates, single cells, and metagenomic data has been made possible by advances in computational methods and substantial reductions in sequencing costs. Although this increasing breadth of draft genomes is providing key information regarding the evolutionary and functional diversity of microbial life, it has become impractical to finish all available reference genomes. Making robust biological inferences from draft genomes requires accurate estimates of their completeness and contamination. Current methods for assessing genome quality are ad hoc and generally make use of a limited number of “marker” genes conserved across all bacterial or archaeal genomes. Here we introduce CheckM, an automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes. We demonstrate the effectiveness of CheckM using synthetic data and a wide range of isolate-, single-cell-, and metagenome-derived genomes. CheckM is shown to provide accurate estimates of genome completeness and contamination and to outperform existing approaches. Using CheckM, we identify a diverse range of errors currently impacting publicly available isolate genomes and demonstrate that genomes obtained from single cells and metagenomic data vary substantially in quality. In order to facilitate the use of draft genomes, we propose an objective measure of genome quality that can be used to select genomes suitable for specific gene- and genome-centric analyses of microbial communities.

HDR-VQM: An objective quality measure for high dynamic range video

High dynamic range (HDR) signals fundamentally differ from the traditional low dynamic range (LDR) ones in that pixels are related (proportional) to the physical luminance in the scene (i.e. scene-referred). For that reason, the existing LDR video quality measurement methods may not be directly used for assessing quality in HDR videos. To address that, we present an objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition. Video quality is then computed based on a spatio-temporal analysis that relates to human eye fixation behavior during video viewing. Consequently, the proposed method does not involve expensive computations related to explicit motion analysis in the HDR video signal, and is therefore computationally tractable. We also verified its prediction performance on a comprehensive, in-house subjective HDR video database with 90 sequences, and it was found to be better than some of the existing methods in terms of correlation with subjective scores (for both across sequence and per sequence cases). A software implementation of the proposed scheme is also made publicly available for free download and use.

Departmental mortality review and present on admission indicator reporting improves observed-to-expected mortality ratios

Objectives: Objective measures have become an increasingly important tool used by organizations to improve health care delivery. Accurate documentation of diagnoses present on admission (POA) is another quality measure that accurately establishes acute and chronic illnesses that might affect prediction of mortality. We sought to determine whether standardized methods of documenting diagnoses POA can improve performance and accuracy of objective quality measures.

AN EFFECTIVE EVALUATION STUDY OF OBJECTIVE MEASURES USING SPECTRAL SUBTRACTIVE ENHANCED SIGNAL

Unwanted noises have a negative influence over communication because it disturbs the conversation and make the communication impossible. Speech enhancement algorithms are used for improving the quality and intelligibility or to reduce listener fatigues. Assessment of speech quality can be done by using either subjective listening test or objective quality measure. Evaluation of several objective measures with the speech processed by enhancement algorithms has been performed but these having limitations to assess original speech signal. This paper represents the study of speech quality measures and compute the values used for regression analyses of the objective measures evaluation study using spectral subtraction algorithm based enhanced speech signal.

Image denoising by exploring external and internal correlations.

Single image denoising suffers from limited data collection within a noisy image. In this paper, we propose a novel image denoising scheme, which explores both internal and external correlations with the help of web images. For each noisy patch, we build internal and external data cubes by finding similar patches from the noisy and web images, respectively. We then propose reducing noise by a two-stage strategy using different filtering approaches. In the first stage, since the noisy patch may lead to inaccurate patch selection, we propose a graph based optimization method to improve patch matching accuracy in external denoising. The internal denoising is frequency truncation on internal cubes. By combining the internal and external denoising patches, we obtain a preliminary denoising result. In the second stage, we propose reducing noise by filtering of external and internal cubes, respectively, on transform domain. In this stage, the preliminary denoising result not only enhances the patch matching accuracy but also provides reliable estimates of filtering parameters. The final denoising image is obtained by fusing the external and internal filtering results. Experimental results show that our method constantly outperforms state-of-the-art denoising schemes in both subjective and objective quality measurements, e.g., it achieves >2 dB gain compared with BM3D at a wide range of noise levels.

Using objective measures of stove use and indoor air quality to evaluate a cookstove intervention in rural Uganda

Exposure to combustion byproducts from cooking is a major health concern globally. Alternative stoves may reduce the burden of disease associated with exposure to household air pollution. We subsidized Ugastove-brand rocket stoves to 54 households in six rural Ugandan villages. We monitored kitchen concentrations of fine particles (PM2.5) and carbon monoxide (CO) before and one month after introduction of the Ugastove. Temperature data-loggers were affixed to each Ugastove and to the traditional stove (three‐stone fire) during the 1-month Ugastove acclimation period to record temporal patterns in stove use and adoption. Household surveys were administered to collect household information that may impact stove use or indoor air quality. PM2.5 kitchen concentrations were 37% lower after introduction of the Ugastove (mean reduction: 0.68mg/m3; 95% confidence interval [CI]: 0.2–1.2; p<0.01). Changes in CO concentrations were small (8% lower; mean reduction: 1.4ppm, 95% CI: –5.2–7.9) and not statistically significant. During the 1-month acclimation period, 47% of households used primarily the Ugastove, 12% used primarily the three stone fire, and 41% used both stoves in tandem. PM2.5 concentrations were generally lowest in households that used primarily the Ugastove, followed by households that used stoves in tandem and that primarily used a three‐stone fire. In summary, introduction of the Ugastove in 54 rural Ugandan households was associated with modest reductions in kitchen concentrations of PM2.5 but not CO. Objective measures of stove use reveal that short-term stove use varied by household.

Frame Loss Concealment for Multiview Video Transmission Over Wireless Multimedia Sensor Networks

In multiview video (MVV) transmission over wireless multimedia sensor networks (WMSNs), a highly compressed video stream is very susceptible to whole-frame losses due to wireless channel errors. Currently, most frame concealment methods can only handle 2-D video or 3-D video with depth, since a lost motion vector can be recovered utilizing the relationship between the color frames and their corresponding depth map. However, depth generation is infeasible in the scenario of WMSNs due to energy constraints and computational limitations of low-power video sensor nodes. In this paper, we tackle the problem of concealing lost whole frames for multiview 3-D video transmission over WMSNs. We propose a new technique, which is capable of dealing with this important problem without the aid of depth map. Experimental results are presented to demonstrate that the proposed algorithm is able to effectively conceal frames of MVV and significantly outperforms other techniques in both objective and subjective quality measurements.

A Novel Image Compression Algorithm Based on Multitree Dictionary and Perceptual-based Rate-Distortion Optimization

This paper presents a novel block-based image coding algorithm which applies a tree-structured multitree dictionary and a perceptual-based rate distortion optimization scheme. While multitree dictionary is employed to support a very large number of different tilings, perceptual-based rate distortion optimization utilizes the SSIM metric, instead of popular MSE metric, to allocate bit rate according to human visual system. Experimental results show that our proposed method outperforms many existing techniques in both subjective and objective image quality measures.

Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions

It is well known that the eating quality of beef has a significant influence on the repurchase behavior of consumers. There are several key factors that affect the perception of quality, including color, tenderness, juiciness, and flavor. To support consumer repurchase choices, there is a need for an objective measurement of quality that could be applied to meat prior to its sale. Objective approaches such as offered by spectral technologies may be useful, but the analytical algorithms used remain to be optimized. For visible and near infrared (VISNIR) spectroscopy, Partial Least Squares Regression (PLSR) is a widely used technique for meat related quality modeling and prediction. In this paper, a Support Vector Machine (SVM) based machine learning approach is presented to predict beef eating quality traits. Although SVM has been successfully used in various disciplines, it has not been applied extensively to the analysis of meat quality parameters. To this end, the performance of PLSR and SVM as tools for the analysis of meat tenderness is evaluated, using a large dataset acquired under industrial conditions. The spectral dataset was collected using VISNIR spectroscopy with the wavelength ranging from 350 to 1800 nm on 234 beef M. longissimus thoracis steaks from heifers, steers, and young bulls. As the dimensionality with the VISNIR data is very high (over 1600 spectral bands), the Principal Component Analysis (PCA) technique was applied for feature extraction and data reduction. The extracted principal components (less than 100) were then used for data modeling and prediction. The prediction results showed that SVM has a greater potential to predict beef eating quality than PLSR, especially for the prediction of tenderness. The infl uence of animal gender on beef quality prediction was also investigated, and it was found that beef quality traits were predicted most accurately in beef from young bulls.

Hotelling trace criterion as a figure of merit for the optimization of chromatogram alignment

We present a methodology for optimization of chromatogram alignment using a class separability measure called the Hotelling trace criterion (HTC). This metric is a multi‐class distance measure that accounts for within‐class and between‐class variation. We chose the correlation optimized warping algorithm as our alignment method and used the HTC to judge the effectiveness of the alignment based on algorithm parameters called segment length and max warp.Biodiesel feedstock samples representing classes of soy, canola, tallow, waste grease, and hybrid were used in our experiments. Fatty acid methyl esters in each biodiesel were separated using gas chromatography‐mass spectroscopy. The entire data set was baseline corrected, aligned, normalized, and mean‐centered prior to principal components (PCs) analysis. The aligned, baseline corrected data sets were used to compute a figure of merit called warping effect, while the PC‐transformed data sets were used to evaluate the HTC. The segment length and max warp parameters that maximized the warping effect and/or HTC were then determined. Scores plots of pairs of PCs, along with 95% confidence ellipses, were created and analyzed.The results demonstrated that the parameters derived from maximizing the HTC more effectively aligned the data, as evidenced by better clustering of the biodiesels in the scores plots. This behavior was robust to the number of PCs used in the computation of the HTC. We conclude that the HTC is an objective measure of alignment quality that allows for optimal class separability and can be applied to optimize other methods of chromatogram alignment. Copyright © 2015 John Wiley &amp; Sons, Ltd.

High cardiac vagal control is related to better subjective and objective sleep quality

Cardiac vagal control (CVC) has been linked to both physical and mental health. One critical aspect of health, that has not received much attention, is sleep. We hypothesized that adults with higher CVC – operationalized by high-frequency heart rate variability (HF-HRV) – will exhibit better sleep quality assessed both subjectively (i.e., with Pittsburgh Sleep Quality Index) and objectively (i.e., with polysomnography). HF-HRV was measured in 29 healthy young women during an extended neutral film clip. Participants then underwent full polysomnography to obtain objective measures of sleep quality and HF-HRV during a night of sleep. As expected, higher resting HF-HRV was associated with higher subjective and objective sleep quality (i.e., shorter sleep latency and fewer arousals). HF-HRV during sleep (overall or separated by sleep phases) showed less consistent relationships with sleep quality. These findings indicate that high waking CVC may be a key predictor of healthy sleep.