The utilization of magnetic resonance (MR) imaging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis. However, the efficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation modeling and MRI image segmentation. To address these challenges, we developed and evaluated a deep learning-based approach for MR-guided 3D NIRST image reconstruction (DL-MRg-NIRST). Using a network trained on synthetic data, the DL-MRg-NIRST system reconstructed images from data acquired during 38 clinical imaging exams of patients with breast abnormalities. Statistical analysis of the results demonstrated a sensitivity of 87.5%, a specificity of 92.9%, and a diagnostic accuracy of 89.5% in distinguishing pathologically defined benign from malignant lesions. Additionally, the combined use of MRI and DL-MRg-NIRST diagnoses achieved an area under the receiver operating characteristic (ROC) curve of 0.98. Remarkably, the DL-MRg-NIRST image reconstruction process required only 1.4 seconds, significantly faster than state-of-the-art MR-guided NIRST methods.

Near-infrared spectral tomography (NIRST) is a non-invasive imaging technique that provides functional information about biological tissues. Due to diffuse light propagation in tissue and limited boundary measurements, NIRST image reconstruction presents an ill-posed and ill-conditioned computational problem that is difficult to solve. To address this challenge, we developed a reconstruction algorithm (Model-CNN) that integrates a diffusion equation model with a convolutional neural network (CNN). The CNN learns a regularization prior to restrict solutions to the space of desirable chromophore concentration images. Efficacy of Model-CNN was evaluated by training on numerical simulation data, and then applying the network to physical phantom and clinical patient NIRST data. Results demonstrated the superiority of Model-CNN over the conventional Tikhonov regularization approach and a deep learning algorithm (FC-CNN) in terms of absolute bias error (ABE) and peak signal-to-noise ratio (PSNR). Specifically, in comparison to Tikhonov regularization, Model-CNN reduced average ABE by 55% for total hemoglobin (HbT) and 70% water (H $_{\mathbf {{2}}}$ O) concentration, while improved PSNR by an average of 5.3 dB both for HbT and H $_{\mathbf {{2}}}$ O images. Meanwhile, image processing time was reduced by 82%, relative to the Tikhonov regularization. As compared to FC-CNN, the Model-CNN achieved a 91% reduction in ABE for HbT and 75% for H $_{\mathbf {{2}}}$ O images, with increases in PSNR by 7.3 dB and 4.7 dB, respectively. Notably, this Model-CNN approach was not trained on patient data; but instead, was trained on simulated phantom data with simpler geometrical shapes and optical source-detector configurations; yet, achieved superior image recovery when faced with real-world data.

As skeleton data becomes increasingly available, Graph Convolutional Networks (GCNs) are popularly adapted to extract the spatial and temporal features for skeleton-based action recognition. However, there are still limitations to be addressed in GCN-based methods. First, the multi-level semantic features fail to be connected, making fine-grained information loss as the network deepens. Second, the cross-scale spatiotempral features fail to be simultaneously considered and refined to focus on informative areas. These limitations lead to the challenge in distinguishing the confusing actions. To address these issues, we propose a cross-scale connection (CSC) structure and a spatiotemporal refinement focus (STRF) module. The CSC aims to bridge the gap between multi-level semantic features. The STRF module refines the cross-scale spatiotemporal features to focus on informative joints in each frame. Both are embedded into the standard GCNs to form the cross-scale spatiotemporal refinement network (CSR-Net). Our proposed CSR-Net explicitly models the cross-scale spatiotemporal information among multi-level semantic representations to boost the distinguishing capability for ambiguous actions. We conduct extensive experiments to demonstrate the effectiveness of our proposed method and it outperforms state-of-the-art methods on the NTU RGB+D 60, NTU-RGB+D 120 and NW-UCLA datasets.

Video is often compressed by standard compression algorithms to facilitate storage and transmission. Compressed video will produce artifacts that affect the video quality. How to improve the quality of compressed video during video post-processing has become an important topic in the multimedia field. This paper proposes a Spatio-Temporal Information Fusion Network for quality enhancement of compressed video, as shown in Fig. 1. The algorithm comprises two parts. In the first part, we use 3D convolution to build a U-shaped network to model the temporal dynamics between input frames. We concatenate features with the same spatial resolution from shallow layers to deep layers by skip-connecting merging channels, which helps the local information of shallow- generated features to reach the output. In the second part, we designed a quality enhancement module to fully mine the spatio-temporal information extracted in the first part, cut the feature map on the time dimension t, and then extract the feature map separately on the spatial dimension and refine the feature. The network is trained in an end-to-end manner, and the data sets are selected from the database Xiph (Xiph.org) and VQEG. We use the H.265/HEVC reference software HM16.5 to compress the video to evaluate the performance of the model under different compression levels. The experimental results show that the average PSNR of 18 HEVC standard sequences is improved by 0.88 dB, 0.86 dB, 0.81 dB and 0.72 dB when the quantization parameters(QP) are equal to 37, 32, 27 and 22, respectively, and the number of parameters are only 0.66 million.

Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels, it is important to appropriately impute the high-dimensional modal missing values. However, most existing imputation methods focus on multivariate time series, fail to simultaneously consider temporal dependencies within each series and the correlations across the series, and also lack a probabilistic interpretation. In this paper, we propose a novel method, which uses a new structured variational approximation technique for the imputation of missing values in multi-modal time series. Instead of directly imputing high-dimensional modal missing values, we use the variational approximation technique to impute intermediate lower-dimensional feature representations of high-dimensional modal missing values from simple modalities related to high-dimensional modality and then feed them into a dynamical model. The dynamical model captures the temporal dependencies of the feature representations and finally predicts the target labels. In order to address the optimization difficulties caused by the lack of ground truth values of lower-dimensional feature representations, we also propose a two-stage isolated optimization strategy for better convergence. We evaluate our method on a real-world stream monitoring dataset. Our extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in both data imputation and prediction performance.

Existing block-based encoding frameworks often use inaccurate quantification and motion compensation techniques, which result in many compression artifacts due to the loss of high-frequency information. In particular, the blurring of content edges and significant compression distortion can negatively impact the subjective video quality given limited coding resources. Hence, there is an urgent need to build a quality enhancement method for improving the quality of the compressed video at the receiving end given the same coding resources.

Abstract The Three‐Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two‐dimensional video coding standard HEVC and used to encode multi‐view plus depth format video. This paper proposes an algorithm based on eXtreme Gradient Boosting to solve the problem of high inter‐frame coding complexity in 3D‐HEVC. Firstly, explore the correlation between the division depth of the inter‐frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter‐frame coding tree unit based on the eXtreme Gradient Boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM‐16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.

How to obtain higher quality reconstructed video within limited coding resources is a research focus for video coding. First, the fluctuating quality, missing pixel and position fluctuation characteristics of the compressed video are found in this paper based on the block-based coding frameworks. Then, in order to reduce the degradation of compressed video quality caused by the above three characteristics, an non-aligned 3D-CNNs for compressed video quality enhancement called "3D-CVQE" is proposed, which can preserve and utilize limited input information in both temporal and spatial domains effectively. Finally, the experiments results validate the effectiveness and generalization ability of the proposed 3D-CVQE approach in the quality enhancement of compressed video.

In video coding, it is always an intractable problem to compress high frequency components including noise and visually imperceptible content that consumes large amount bandwidth resources while providing limited quality improvement. Direct using of denoising methods causes coding performance degradation, and hence not suitable for video coding scenario. In this work, we propose a video pre-processing approach by leveraging edge preserving filter specifically designed for video coding, of which filter parameters are optimized in the sense of rate-distortion (R-D) performance. The proposed pre-processing method removes low R-D cost-effective components for video encoder while keeping important structural components, leading to higher coding efficiency and also better subjective quality. Comparing with the conventional denoising filters, our proposed pre-processing method using the R-D optimized edge preserving filter can improve the coding efficiency by up to −5.2% BD-rate with low computational complexity.

As an essential part of the advanced driving assistant system, visual localization technology has drawn much attention. To solve the interference caused by the high similarity of scenes at a high frame rate, this paper proposes a localization method based on global-local features and keyframe retrieval. In this method, the semantic features of the scene extracted by the fused semantic segmentation model are used to guide the detection of the key regions with discriminative information. By using the screening strategy based on pixel position cues and unsupervised learning algorithm, the binary feature descriptor with strong discrimination power is extracted, which can improve the matching accuracy and reduce the computational complexity. Secondly, combining the keyframes obtained by calculating scene discriminative score with a sequence matching algorithm can promote localization performance. The experiments conducted on the Nordland dataset and the challenging Hong Kong light rail dataset. The experiment results show that our method has higher computing efficiency and locating accuracy than the state-of-the-art ConvNet featurebased localization method named SeqCNNSLAM under the condition of drastic changes in appearance. Moreover, compared with SeqSLAM which is based on global features, the precision of our approach has significant improvement while meeting real-time requirements.

View synthesis optimization (VSO) introduces heavy computational complexity caused by the VSO-based iterative search of all possible quad-tree partitions. To reduce the complexity, this paper proposes a convolutional neural network (CNN) scheme based on layer-classification for fast depth intra coding. First, a layer-classification model based on texture smoothness is proposed to determine the smoothest depth map. Then, a CNN network incorporating SENet (CNN-SENet) structure is designed and trained. Finally, the layer-classification model and the CNN-SENet network are combined to predict the coding unit (CU) partition of all coding units (CUs) for depth map at a specific view.

3D-High Efficiency Video Coding (3D-HEVC) is a video compression standard developed for multi-view video plus depth map coding based on the latest HEVC coding standard. We propose an eXtreme Gradient Boosting (XGBoost) system based fast coding unit (CU) level decision for depth maps, which is used to solve the problem of high coding complexity caused by the addition of depth maps and new coding tools in 3D-HEVC. We explore the application of data mining and machine learning in video coding by using texture feature attributes that are highly correlated with CU size. The algorithm is mainly divided into three parts as shown in Figure 1. The algorithm comprises two parts: Models training and fast CU segmentation decision. In the first part, we use data mining and machine learning to construct the decision models by using the texture information of the depth maps as the feature attribute vectors and whether the current CU continues to be divided into sub-CUs as class labels. In the second part, feature attributes were extracted from the coding process, and the trained models were used to decide if the CU continues to partition. Experimental results demonstrated that proposed algorithm yields average 43.52% encoding time reduction with 0.12% BD-rate decrease on V/T and 0.37% increase on S/T under the all intra configuration, compared with the reference software HTM-16.0. In addition, compared with related work, the proposed method achieves different degrees of improvement in coding performance.

Compared with traditional High Efficiency Video Coding (HEVC), 3D-HEVC introduces multi-view coding and depth map coding, which leads to significant increase in coding complexity. In this paper, we propose a low complexity intra coding algorithm for depth map based on end-to-end edge detection network. Firstly, we use Holistically Nested Edge Detection (HED) network to determine the edge location of the depth map. Secondly, we use Ostu method to divide the output of the HED into foreground region and background region. Finally, the CU size and the candidate list of intra mode are determined according to the region of coding tree unit (CTU). Experimental results demonstrate that the proposed algorithm can reduce the encoding time by 39.56% on average under negligible degradation of coding performance.

Hierarchical prediction structures applied in High Efficiency Video Coding (H.265/HEVC) leads to significant coding efficiency improvement. Quantization parameter cascading (QPC) techniques designed for specific prediction structure employs different quantization intervals for different temporal layers that further improve rate-distortion (R-D) performance of hierarchical coding. The existing QPC schemes focus on quantization parameter allocation, however, ignore the adaptation of Lagrangian multiplier to prediction structure, inter-layer residual dependency and layer-different quantization parameters (QPs). The misalignment of employed Lagrangian multiplier to the optimal one restricts the performance of QPC methods. To promote QPC schemes to their best performance, we propose a Lagrangian multiplier adaptation method based on accurate hyperbolic R-D model, distortion-QP model and rate-QP model. The derived Lagrangian multiplier is adaptive to residual characteristics under different prediction structures and QPC methods. Experimental results show that our proposed Lagrangian multipliers applied to existing QPC methods achieves Bjontegaard delta bit rate (BDBR) gain of 3.59% to 4.65% with small computational complexity increase.

High-efficiency video coding (HEVC) poses a considerable challenge to hardware implementation due to its complexity. Mobile devices are powered by batteries that are limited in capacity. Therefore, reducing the power consumption arising from the implementation of sophisticated coding tools in HEVC is an especially important issue for mobile devices. In particular, motion estimation (ME) is the major contributor to the power consumption of the encoder and the calculation of the sum of absolute difference (SAD) for ME consumes more than 50% of the total ME power. In this paper, a low-power motion estimation VLSI architecture is proposed based on a novel method of calculating the SAD. By reusing the calculation, the computation complexity and, hence, the power consumption are reduced. A low-power systolic processing elements array and a novel memory hierarchy are developed, which enable real-time processing of 8K resolution video with only half of the power consumption when compared with the state-of-the-art design.

As an upgrade for H.264/AVC, high efficiency video coding (HEVC) achieves 50% bitrate reduction under the equivalent visual quality. However, high computational complexity increases dramatically for adopting up to 35 intra prediction modes. To deal with this issue, we explore spatial-temporal correlation between PUs to narrow rough mode decision (RMD) candidate list and rate distortion optimization (RDO) candidate list respectively. A fast PU intra mode decision scheme is proposed for HEVC fast intra encoding. For RMD, the proposed scheme early determines the impossible range of the PU optimal mode in line with spatial statistical analysis theory. It would narrow RMD candidate list and speed up RMD process. First, 4 subsets (Sb1, Sb2, Sb3 and Sb4) are defined in mode set with 33 directional prediction modes as shown in Figure 1. And then, on the basis of spatial statistical analysis theory, the impossible range of the parent PU optimal mode would be judged by its known optimal mode. Last, low probability subsets of current PU are removed, and candidate subsets are determined for current PU. Where ModeParBest and ModeCurBest are the parent PU optimal mode and the current PU optimal mode respectively. For RDO, the proposed scheme tries to combine temporal correlation with intra coding, which adds co-located optimal mode of the previous frame into the RDO list of the current PU. At the same time, the modes in RDO list are decreased focusing on most time-consuming PUs (4 4 PUs, 8 8 PUs) from 8 candidate modes to 3 candidate modes. Experimental results demonstrate that the proposed scheme yields average 29% encoding time reduction with average 1.19% BDBR gain and 0.06dB BDPSNR loss compared with HM16.9. Further, the proposed method implements fast CU encoding without additional computation during the encoding process.

3D-High Efficiency Video Coding (3D-HEVC) contains more encoding viewpoints than traditional HEVC, resulting in a significant increase of coding complexity. In this paper, we propose a low complexity intra-mode selection algorithm to reduce the number of intra-modes by detecting the flat area and texture direction of the depth map. The corresponding intra-prediction modes are skipped when the flat region condition is satisfied. Otherwise, the direction of the edge is detected to decrease the number of angle modes in RMD, which can reduce the intra-coding complexity and coding time cost. Experimental results demonstrate that the proposed algorithm achieves on average 36.48% time saving with negligible degradation of coding performance.

BACKGROUND: Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics. RESULTS: We present a unified hybrid self-attention deep learning framework, namely HybridAtt, to automatically classify sleep stages by capturing channel and temporal correlations from multivariate PSG records. We construct a new multi-view convolutional representation module to learn channel-specific and global view features from the heterogeneous PSG inputs. The hybrid attention mechanism is designed to further fuse the multi-view features by inferring their dependencies without any additional supervision. The learned attentional representation is subsequently fed through a softmax layer to train an end-to-end deep learning model. CONCLUSIONS: We empirically evaluate our proposed HybridAtt model on a benchmark PSG dataset in two feature domains, referred to as the time and frequency domains. Experimental results show that HybridAtt consistently outperforms ten baseline methods in both feature spaces, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.

The recent advances in pervasive sensing technologies have enabled us to monitor and analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to prevent serious outcomes caused by epileptic seizures. To avoid manual visual inspection from long-term EEG readings, automatic EEG seizure detection has garnered increasing attention among researchers. In this paper, we present a unified multi-view deep learning framework to capture brain abnormalities associated with seizures based on multi-channel scalp EEG signals. The proposed approach is an end-to-end model that is able to jointly learn multi-view features from both unsupervised multi-channel EEG reconstruction and supervised seizure detection via spectrogram representation. We construct a new autoencoder-based multi-view learning model by incorporating both inter and intra correlations of EEG channels to unleash the power of multi-channel information. By adding a channel-wise competition mechanism in the training phase, we propose a channel-aware seizure detection module to guide our multi-view structure to focus on important and relevant EEG channels. To validate the effectiveness of the proposed framework, extensive experiments against nine baselines, including both traditional handcrafted feature extraction and conventional deep learning methods, are carried out on a benchmark scalp EEG dataset. Experimental results show that the proposed model is able to achieve higher average accuracy and f1-score at 94.37% and 85.34%, respectively, using 5-fold subject-independent cross validation, demonstrating a powerful and effective method in the task of EEG seizure detection.

In the high efficiency video coding (HEVC) standard, motion estimation (ME) adopts a quadtree coding structure and a larger search range to improve the coding performance. These advanced coding tools, however, dramatically increase the computational complexity. To accelerate ME, fast methods have been proposed that reduce ME complexity at the expense of rate-distortion (R-D) performance. However, none of these methods can claim their tradeoff to be optimal. In this paper, we propose an optimal fast motion estimation (FME) algorithm based on an analytical model of rate-complexity-distortion (R-C-D). We extend the traditional R-D model by introducing the ME complexity into it, which enables us to explicitly express the R-D performance under different complexity budgets. Based on the R-C-D model, the proposed FME finds the R-C-D optimized search ranges for some representative prediction units (PUs), which are then extended or refined dynamically according to motion characteristics for neighboring PUs. The proposed FME enables an R-D performance close to that of full search. When compared with the default FME method in the reference software of HEVC, the proposed fast algorithm can reduce the complexity by over 80% while improving the R-D performance. Furthermore, our proposed FME algorithm is hardware friendly, as regular data flow enables high data reuse efficiency.

Recent advances in attention networks have gained enormous interest in time series data mining. Various attention mechanisms are proposed to soft-select relevant timestamps from temporal data by assigning learnable attention scores. However, many real-world tasks involve complex multivariate time series that continuously measure target from multiple views. Different views may provide information of different levels of quality varied over time, and thus should be assigned with different attention scores as well. Unfortunately, the existing attention-based architectures cannot be directly used to jointly learn the attention scores in both time and view domains, due to the data structure complexity. Towards this end, we propose a novel multi-view attention network, namely MuVAN, to learn fine-grained attentional representations from multivariate temporal data. MuVAN is a unified deep learning model that can jointly calculate the two-dimensional attention scores to estimate the quality of information contributed by each view within different timestamps. By constructing a hybrid focus procedure, we are able to bring more diversity to attention, in order to fully utilize the multi-view information. To evaluate the performance of our model, we carry out experiments on three real-world benchmark datasets. Experimental results show that the proposed MuVAN model outperforms the state-of-the-art deep representation approaches in different real-world tasks. Analytical results through a case study demonstrate that MuVAN can discover discriminative and meaningful attention scores across views over time, which improves the feature representation of multivariate temporal data.

Epileptic seizure detection has gained increasing attention in clinical therapy. Scalp electroencephalogram (EEG) analysis is a common way to capture brain abnormality for seizure onset detection. This paper presents a novel context-learning based approach using multi-feature fusion to compensate for incomplete description of single feature in epileptic EEG signals. First, EEG scalogram sequence is generated using wavelet transform to represent the time-frequency information. Second, three sets of EEG context features are unsupervisedly learned in parallel by using global principal component analysis (GPCA), stacked denoising autoencoders (SDAEs) and EEG embeddings, respectively. Finally, the multi-features are concatenated into a fixed-length feature vector for seizure classification. The experimental results conducted on two real EEG datasets demonstrate that the proposed cross-patient learning model is able to extract meaningful context features from different perspectives, and hence can detect the onset of epileptic seizure effectively.

Time series data mining has gained increasing attention in health domain. Recently, researchers attempt to employ Natural Language Processing (NLP) to health data mining, in order to learn proper representations of discrete medical concepts from Electronic Health Records (EHRs). However, existing models do not take continuous physiological records into account, which are naturally existed in EHRs. The major challenges for this task are to model non-obvious representations from observed high dimensional biosignals, and to interpret the learned features. To address these issues, we propose Wave2Vec, an end-to-end deep learning model, to bridge the gap between biosignal processing and language modeling. Wave2Vec jointly learns both inherent and embedding representations of biosignals at the same time. To evaluate the performance of our model in clinical task, we carry out experiments on two real world benchmark biosignal datasets. Experimental results show that the proposed Wave2Vec model outperforms the six feature leaning baselines in biosignal processing.

Quadtree brings extremely high computational complexity in high efficiency video coding (HEVC). Innovative works for improving quadtree structures to further reduce encoding time are stated in this paper. A novel quadtree probability mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and video content change. Secondly, a CU quadtree probability model is proposed for modeling and predicting CU partition based on the group of picture (GOP). Eventually, a CU quadtree probability update is proposed, aiming to address probabilistic model distortion problems caused by video content change. Experimental results have shown that the proposed CU quadtree probability mechanism significantly outperforms HEVC by considerably reducing encoding time by 27% for lossy coding and 42% for (visually) lossless coding, without compromising rate-distortion (RD) performance.

In this paper, we propose an improved Largest Coding Unit (LCU) level rate control algorithm for the 3D video coding extension of the high efficiency video coding (3D-HEVC). In the proposed algorithm, bit allocation for extended views is investigated in combination with temporal and inter-view Mean Absolute Difference (MAD) predictions. Upon prediction weights generated by MAD prediction errors, accurate bitrate for LCU can be obtained. Then, a weight-based R-λ model is designed based on the prediction weights by improving the model parameters consistent with video content. Experimental results show that the proposed algorithm can achieve better rate distortion performance and bitrate accuracy in comparison with the R-λ model based rate control benchmark algorithm in HTM.

With the development of healthcare technology and medical standard, the demands of the quality and efficiency of mobile nursing are increased significantly, hence it is necessary to improve hospital nursing mechanism dealing with massive tasks. In this paper, we develop an optimized mobile nursing system based on the specific nurse workflow analysis in obstetrics. An efficient integration system framework is proposed combined with existing hospital common systems and WLAN network environment, which implements automatically execute data interaction. A combination model of C/S using PDA on hand and B/S using PC working at nurse workstation is implemented which ensure the mobility and centrality. A lighten AJAX-SSH2 development framework is used to enhance the function expansibility. The proposed system has been applied in a regional obstetrics hospital in China. Practical clinical application results prove that the proposed system can raise nursing efficiency and reduce medical error rate, make it possible to nurses paying more attention on patients. The healthcare big data collected by this system has considerable value for further research.

The quadtree is one of the most advanced techniques contributing to the excellent compression performance of high efficiency video coding (HEVC). However, the computational complexity increases because the quadtree examines all coding unit (CU) sizes to obtain the optimal CU partitioning. This paper focuses on quadtree degeneration based on a proposed quadtree probability mechanism. Two techniques, a quadtree probability model (QPM) procedure and a quadtree probability update (QPU) procedure, are proposed. The QPM process estimates a CU distribution model based on a quantization parameter (QP) and a group of pictures (GOP). Based on the model, a new quadtree is constructed by skipping low probability tree nodes. The QPU process is performed to update the new quadtree based on scene content change. Update addresses model distortion and ensures the accuracy of the new quadtree. Experimental results demonstrate that the proposed quadtree probability mechanism for quadtree degeneration considerably reduces average encoding time (27.55%) for the low delay condition. Applied to lossless coding, the proposed mechanism achieves a significant 43.10% encoding time reduction. The experiments also show that the proposed quadtree probability mechanism improves HEVC coding efficiency for a variety of applications and sequence characteristics.

This paper presents view synthesis optimization for 3D-HEVC based on a new texture smoothness process. In the original method, all pixels are exhaustively rendered to get distortions from synthesized views. Since not all pixels from the distorted depth map may cause distortions in the synthesized view, it brings unnecessary coding complexity. In this paper, lines of pixels are skipped based on the analysis of pixel regularity from smooth texture regions. It is due to the fact that the distorted disparity may not have much effect on the synthesized view in smooth texture regions. The proposed method can reduce the coding complexity of view synthesis optimization without significant performance loss.

This paper proposes a novel fast disparity vector derivation algorithm to get the neighbor block disparity vector (NBDV) for 3D High Efficiency Video Coding (3D-HEVC) texture inter-view coding. In the current 3D-HEVC design, it is observed that the simplified CU (coding unit)-based disparity vector derivation method is faster than the method of full searching, but the quality of the derived vector can be improved. A novel group searching and proportion combination based disparity vector derivation algorithm is proposed in this paper that improves the accuracy of the NBDV and maintains the process efficiency in the meantime. The test results of the proposed algorithm report maximum 0.04% PSNR/bitrate improving and 0.05% bit-saving with the same efficiency of the existing fast method under the Common Testing Condition (CTC).

Critical node detection in dynamic networks is of great value in many areas, such as the evolving of friendship in social networks, the development of epidemics, molecular pathogenesis of diseases and so on. As for detecting critical nodes in dynamic Protein-Protein Interaction Networks (PPINs), there are mainly two challenges: the first is to construct the dynamic PPINs that are not available directly from biological experiments in laboratories; and the second is how to identify the most critical units that are responsible for the dynamic processes. This paper proposes effective framework to tackle these two problems. First of all, this paper proposes to construct the dynamic PPINs by simultaneously modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. As result, more comprehensive dynamic PPINs are built. Besides, a novel critical protein detection method that integrates multiple PPI networks into a Deep Belief Network model (referred to as MIDBN) is developed. The integrated model is trained to get hierarchical common representations of multiple sources which are used to reconstruct the original data. The variabilities of the reconstruction errors across the time courses are ranked to finally get the top proteins that have significantly different evolving structural patterns than the other nodes in the dynamic networks. We evaluated our network construction method by comparing the functional representations of the derived networks with that of two other traditional construction methods, and our method achieved superior function analysis results. The ranking results of critical proteins from MIDBN were compared with results from two baseline methods and the comparison results showed that MIDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

In this paper, a fast algorithm is proposed to speed up the motion and disparity estimation in stereoscopic video coding. Based on the stereo-motion consistency constraint, an iterative search strategy is suggested to get the motion and disparity vectors simultaneously. A credible base vector selection scheme and an adaptive search range adjustment technique are designed to further strengthen the iterative search. Results show that the complexity can be significantly reduced compared to the JMVM full search with a negligible quality drop.

This paper presents an efficient intra mode decision scheme for down-sizing video transcoding in H.264 using support vector machines (SVMs). In order to reduce the high computational complexity of intra prediction in the H.264 re- encoder, the proposed scheme uses SVMs to exploit the correlation between coding information extracted from the input high-resolution bit-stream and the coding modes of macro- blocks in down-sized video, and turns the intra mode decision problem into pattern classification problem to achieve low complexity video transcoding framework. After the SVMs classifier, improbable modes are eliminated and only a small number of candidate modes are carried out using the RDO operations. Hence, remarkable computing time of up to 65.3% can be saved with trivial bit gain and quality loss.