SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072

Satisfied user ratio prediction with support vector regression for compressed stereo images

We propose the first method to predict the Satisfied User Ratio (SUR) for compressed stereo images. The method consists of two main steps. First, considering binocular vision properties, we extract three types of features from stereo images: image quality features, monocular visual features, and binocular visual features. Then, we train a Support Vector Regression (SVR) model to learn a mapping function from the feature space to the SUR values. Experimental results on the SIAT-JSSI dataset show excellent prediction accuracy, with a mean absolute SUR error of only 0.08 for H.265 intra coding and only 0.13 for JPEG2000 compression

Influenza vaccination rates among healthcare workers: a systematic review and meta-analysis investigating influencing factors

IntroductionHealthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.MethodsCross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.ResultsA total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.ConclusionThe global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.Systematic review registrationwww.inplysy.com, identifier: 202350051

Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images

The file attached to this record is the author's final peer reviewed version.The Satisfied User Ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for 2D images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images

Spatial Analysis of Arabidopsis thaliana Gene Expression in Response to Turnip mosaic virus Infection

Virus-infected leaf tissues comprise a heterogeneous mixture of cells at different stages of infection. The spatial and temporal relationships between sites of virus accumulation and the accompanying host responses, such as altered host gene expression, are not well defined. To address this issue, we utilized Turnip mosaic virus (TuMV) tagged with the green fluorescent protein to guide the dissection of infection foci into four distinct zones. The abundance of Arabidopsis thaliana mRNA transcripts in each of the four zones then was assayed using the Arabidopsis ATH1 GeneChip oligonucleotide microarray (Affymetrix). mRNA transcripts with significantly altered expression profiles were determined across gradients of virus accumulation spanning groups of cells in and around foci at different stages of infection. The extent to which TuMV-responsive genes were up- or downregulated primarily correlated with the amount of virus accumulation regardless of gene function. The spatial analysis also allowed new suites of coordinately regulated genes to be identified that are associated with chloroplast functions (decreased), sulfate assimilation (decreased), cell wall extensibility (decreased), and protein synthesis and turnover (induced). The functions of these downregulated genes are consistent with viral symptoms, such as chlorosis and stunted growth, providing new insight into mechanisms of pathogenesis

SUR-FeatNet: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Feature Learning

The file attached to this record is the author's final peer reviewed version.The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson-Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays

Hypoxia-Induced Mitogenic Factor (HIMF/FIZZ1/RELMα) Recruits Bone Marrow-Derived Cells to the Murine Pulmonary Vasculature

. and localized to the media layer of the vessels. This finding suggests that these cells are of mesenchymal origin and differentiate toward myofibroblast and vascular smooth muscle. Structural location in the media of small vessels suggests a functional role in the lung vasculature. To examine a potential mechanism for HIMF-dependent recruitment of mesenchymal stem cells to the pulmonary vasculature, we performed a cell migration assay using cultured human mesenchymal stem cells (HMSCs). The addition of recombinant HIMF induced migration of HMSCs in a phosphoinosotide-3-kinase-dependent manner.These results demonstrate HIMF-dependent recruitment of BMD mesenchymal-like cells to the remodeling pulmonary vasculature