Research Article Visual Perception Based Objective Stereo Image Quality Assessment for 3D Video Communication

Abstract: Stereo image quality assessment is a crucial and challenging issue in 3D video communication. One of major difficulties is how to weigh binocular masking effect. In order to establish the assessment mode more in line with the human visual system, Watson model is adopted, which defines visibility threshold under no distortion composed of contrast sensitivity, masking effect and error in this study. As a result, we propose an Objective Stereo Image Quality Assessment method (OSIQA), organically combining a new Left-Right view Image Quality Assessment (LR-IQA) metric and Depth Perception Image Quality Assessment (DP-IQA) metric. The new LR-IQA metric is first given to calculate the changes of perception coefficients in each sub-band utilizing Watson model and human visual system after wavelet decomposition of left and right images in stereo image pair, respectively. Then, a concept of absolute difference map is defined to describe abstract differential value between the left and right view images and the DP-IQA metric is presented to measure structure distortion of the original and distorted abstract difference maps through luminance function, error sensitivity and contrast function. Finally, an OSIQA metric is generated by using multiplicative fitting of the LR-IQA and DP-IQA metrics based on weighting. Experimental results shows that the proposed method are highly correlated with human visual judgments (Mean Opinion Score) and the correlation coefficient and monotony are more than 0.92 under five types of distortions such as Gaussian blur, Gaussian noise, JP2K compression, JPEG compression and H.264 compression

Human Pose Transfer with Augmented Disentangled Feature Consistency

Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.Comment: 22 pages, 6 figure

A Novel Macroblock Level Rate Control Method for Stereo Video Coding

To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods

New Image Restoration Method Based on Multiple Aperture Defocus Images for Microscopic Images

Abstract: Image deconvolution is an effective image restoration technique to improve the quality of digital microscopic images resulting from out-of-focus blur. To solve the severely ill-posed problem of traditional Richardson-Lucy method, considering the point spread difference of various directions, a new microscope image restoration method based on multiple defocused images of different aperture is proposed. The maximumlikelihood estimation is used to suppress the ringing artifacts and noises sensitivity of microscope image. Experimental results show that the proposed algorithm performs better than Richardson-Lucy method and improve peak-signal-to-noise-rate about 4 dB

Serum retinol-binding protein 4 levels are elevated but do not contribute to insulin resistance in newly diagnosed Chinese hypertensive patients

BACKGROUND: Insulin resistance (IR) is closely correlated with cardiovascular disease (CVD). Retinol-binding protein 4 (RBP4) is a novel adipokine that modulates the action of insulin in various diseases. This study addressed the relationship between RBP4 and IR in newly diagnosed essential hypertension. METHODS: Serum RBP4, anthropometric and metabolic parameters were determined in 267 newly diagnosed essential hypertensive patients not taking antihypertensive medications. The patients along with 64 control (NC) normotensive and lean subjects paired by age and sex were divided into two groups depending on body mass index (BMI), hypertension with obesity (HPO) and hypertension without obesity (HP). RESULTS: A striking difference was observed in RBP4 levels between the HP and NC groups. Significantly higher levels were noted in the HP group compared with the NC group; slightly, but not significantly, lower levels were observed in the HPO group compared with the HP group. After adjusting for BMI, WC and WHR, a modestly linear relationship was observed between RBP4 levels and SBP (r = 0.377; p = 0.00), DBP (r = 0.288; p = 0.00) and HOMA-β(r = 0.121; p = 0.028). Multiple stepwise regression analysis showed that SBP, WHR and drinking were independently related with serum RBP4 levels. CONCLUSIONS: The results of this study indicated that RBP4 levels were increased in naive hypertensive patients; however, no differences were observed in obese or non-obese hypertensive subjects. Our data suggest for the first time that RBP4 levels are significantly increased but do not contribute to the development of IR in newly diagnosed hypertensive Chinese patients

A Fast and Robust Ellipse-Detection Method Based on Sorted Merging

A fast and robust ellipse-detection method based on sorted merging is proposed in this paper. This method first represents the edge bitmap approximately with a set of line segments and then gradually merges the line segments into elliptical arcs and ellipses. To achieve high accuracy, a sorted merging strategy is proposed: the merging degrees of line segments/elliptical arcs are estimated, and line segments/elliptical arcs are merged in descending order of the merging degrees, which significantly improves the merging accuracy. During the merging process, multiple properties of ellipses are utilized to filter line segment/elliptical arc pairs, making the method very efficient. In addition, an ellipse-fitting method is proposed that restricts the maximum ratio of the semimajor axis and the semiminor axis, further improving the merging accuracy. Experimental results indicate that the proposed method is robust to outliers, noise, and partial occlusion and is fast enough for real-time applications

The impact of hyperglycaemic crisis episodes on long-term outcomes for inpatients presenting with acute organ injury: A prospective, multicentre follow-up study

BackgroundThe long-term clinical outcome of poor prognosis in patients with diabetic hyperglycaemic crisis episodes (HCE) remains unknown, which may be related to acute organ injury (AOI) and its continuous damage after hospital discharge. This study aimed to observe the clinical differences and relevant risk factors in HCE with or without AOI.MethodsA total of 339 inpatients were divided into an AOI group (n=69) and a non-AOI group (n=270), and their differences and risk factors were explored. The differences in clinical outcomes and prediction models for evaluating the long-term adverse events after hospital discharge were established.ResultsThe mortality among cases complicated by AOI was significantly higher than that among patients without AOI [8 (11.59%) vs. 11 (4.07%), Q = 0.034] during hospitalization. After a 2-year follow-up, the mortality was also significantly higher in patients with concomitant AOI than in patients without AOI after hospital discharge during follow-up [13 (21.31%) vs. 15 (5.8%), Q < 0.001]. The long-term adverse events in patients with concomitant AOI were significantly higher than those in patients without AOI during follow-up [15 (24.59%) vs. 31 (11.97%), Q = 0.015]. Furthermore, Blood β-hydroxybutyric acid (P = 0.003), Cystatin C (P <0.001), serum potassium levels (P = 0.001) were significantly associated with long-term adverse events after hospital discharge.ConclusionsThe long-term prognosis of HCE patients complicated with AOI was significantly worse than that of HCE patients without AOI. The laboratory indicators were closely correlated with AOI, and future studies should explore the improvement of clinical outcome in response to timely interventions

DNet: Dynamic Neighborhood Feature Learning in Point Cloud

Neighborhood selection is very important for local region feature learning in point cloud learning networks. Different neighborhood selection schemes may lead to quite different results for point cloud processing tasks. The existing point cloud learning networks mainly adopt the approach of customizing the neighborhood, without considering whether the selected neighborhood is reasonable or not. To solve this problem, this paper proposes a new point cloud learning network, denoted as Dynamic neighborhood Network (DNet), to dynamically select the neighborhood and learn the features of each point. The proposed DNet has a multi-head structure which has two important modules: the Feature Enhancement Layer (FELayer) and the masking mechanism. The FELayer enhances the manifold features of the point cloud, while the masking mechanism is used to remove the neighborhood points with low contribution. The DNet can learn the manifold features and spatial geometric features of point cloud, and obtain the relationship between each point and its effective neighborhood points through the masking mechanism, so that the dynamic neighborhood features of each point can be obtained. Experimental results on three public datasets demonstrate that compared with the state-of-the-art learning networks, the proposed DNet shows better superiority and competitiveness in point cloud processing task