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I. INTRODUCTIONBlood pressure (BP) is one of the four vital signs of human body besides heart rate, respiratory rate, and body temperature. Recently, the risk of having hypertension is extending from older to younger groups of the population. This trend making the number of people who need daily monitoring of BP is significantly increased. In addition, the demand for public resources in the hospital is becoming more and more serious and the type of healthcare is tending to individual-centered rather than hospital-centered. The need of self-monitoring BP devices dramatically increases in the recent decades. However, most of the commercial BP measuring devices require to inflate and deflate a cuff for determining the BP values. This measurement has a high demand for the medical practitioner"s operation skills.Self-monitoring BP devices using cuffless and noninvasive detective methods are very attractive for personal healthcare applications. It was reported that Manuscript received August 4, 2016; revised December 9, 2016 cuff-less, noninvasive, and continuous measurement of BP could be achieved by photoplethysmography (PPG) [1]- [7]. PPG [8] is a simple, portable and low-cost optical pulse that can be used to detect blood volume changes. The waveform of PPG is formed by heartbeat and affected by the condition of cardiovascular system [9]. When BP increases, the volumetric elasticity of blood vessel increases. This makes vessel wall stiff and the pulse wave propagation velocity is increased. The pulse wave propagation velocity (PWV) is represented as the pulse wave propagation distance divided by Pulse transit time (PTT). PTT is the time for the pulse wave to travel between two arterial sites, which is highly related with PWV. Normally, PTT is calculated from the time distance between R wave of ECG signal and following peak of fingertip PPG signal [10]. PTT based methods [5]-[7], [11]- [13] have been demonstrated to achieve relatively high accuracy on BP estimation, but two devices are required for the measurement. This is inconvenient and requires high operation skill in practical usage. Moreover, synchronization between two devices is also a big challenge of PTT approach. Therefore, PPG waveform analysis approach using a single PPG signal is very attractive for continuous and noninvasive BP measurement. However, several reported PPG waveform analysis based BP estimation methods [14], [15] From the recent studies [17], [18] of vascular aging through the second derivative of PPG (SDPPG), Pulse Wave Velocity (PWV) and SDPPG contain the information about aortic compliance and stiffness. These factors highly correlated with blood pressure. Basically, SDPPG is the acceleration of PPG and its waveform is in "W" shape with five sequential waves normally, as shown in the lower part of Fig. 1. These five waves are named as Initial Positive Wave (IPW), Early Negative Wave (ENW), Late Upsloping Wave (LUW), Late Downsloping Wave (LDW), and Diastolic Positive Wave (DPW). The positions of "a", "b", "c", "d", and "e" are peaks...

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In this work we propose an efficient general-purpose no-reference (NR) video quality assessment (VQA) framework which is based on 3D shearlet transform and Convolutional Neural Network (CNN). Taking video blocks as input, simple and efficient primary spatiotemporal features are extracted by 3D shearlet transform, which are capable of capturing the Natural Scene Statistics (NSS) properties. Then, CNN and logistic regression are concatenated to exaggerate the discriminative parts of the primary features and predict a perceptual quality score. The resulting algorithm, which we name SACONVA (SheArlet and COnvolutional neural network based No-reference Video quality Assessment), is tested on well-known VQA databases of LIVE, IVPL and CSIQ. The testing results have demonstrated SACONVA performs well in predicting video quality and is competitive with current state-of-the-art full-reference VQA methods and general-purpose NR-VQA algorithms. Besides, SACONVA is extended to classify different video distortion types in these three databases and achieves excellent classification accuracy. In addition, we also demonstrate that SACONVA can be directly applied in real applications such as blind video denoising.Index Terms-Auto-encoder, convolutional auto-encoder, convolutional neural network, distortion identification, no-reference video quality assessment, 3D shearlet transform.

Search patterns and the center-biased characteristics of motion vector distribution (MVD) have large impact on both searching speed and quality of block motion estimation. In this paper. we propase a novel algorithm using a rood-shaped search pattern as the initial step and large/small diamond search patterns as the subsequent steps for fast block motion estimation (BME). The rood-shaped pattern is to fit the rood-center-biased MVD characteristics of the real-world sequences by evaluating the 9 relatively higher probable candidates located as a rood-shaped pattem at the search-grid center. The propased rood-diamond search algorithm (RDS) employs halfway-stop technique and could find small motion vectors with fewer points than the diamond search algorithm (DS) while maintains similar or even better quality. The speedup improvement of RDS over DS can be up to 40% Simulations show that RDS is much more robust. provides faster searching speed and smaller distortions than other fast algorithms.

Given a grayscale photograph, the colorization system estimates a visually plausible colorful image. Conventional methods often use semantics to colorize grayscale images. However, in these methods, only classification semantic information is embedded, resulting in semantic confusion and color bleeding in the final colorized image. To address these issues, we propose a fully automatic Saliency Map-guided Colorization with Generative Adversarial Network (SCGAN) framework. It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding in the colorized image. Since the global features from pre-trained VGG-16-Gray network are embedded to the colorization encoder, the proposed SCGAN can be trained with much less data than state-of-the-art methods to achieve perceptually reasonable colorization. In addition, we propose a novel saliency map-based guidance method. Branches of the colorization decoder are used to predict the saliency map as a proxy target. Moreover, two hierarchical discriminators are utilized for the generated colorization and saliency map, respectively, in order to strengthen visual perception performance. The proposed system is evaluated on ImageNet validation set. Experimental results show that SCGAN can generate more reasonable colorized images than state-of-the-art techniques.