Deep learning has superior performance in defect detection， however， due to the low defect probability， the detection process of defect-free images occupies most of the calculation time， which seriously limits the overall effective detection speed. In order to solve the above problem， a section steel surface defect detection algorithm based on cascade network named SDNet （Select and Detect Network） was proposed. The proposed algorithm was divided into two stages： the pre-inspection stage and the precise detection stage. In the pre-inspection stage， the lightweight ResNet pre-inspection network based on Depthwise Separable Convolution （DSC） and multi-scale parallel convolution was used to determine whether there were defects in the surface image of the section steel. In the precise detection stage， the YOLOv3 was used as the baseline network to accurately classify and locate the defects in the image. In addition， the improved Atrous Spatial Pyramid Pooling （ASPP） module and dual attention module were introduced in the backbone feature extraction network and prediction branches to improve the network detection performance. Experimental results show that the detection speed and the accuracy of SDNet on 1 024 pixel×1 024 pixel images reach 120.63 frames per second and 92.1% respectively. Compared to the original YOLOv3 algorithm， the proposed algorithm has the detection speed of about 3.7 times and the detection precision improved by 10.4 percentage points. The proposed algorithm can be applied to the rapid detection of section steel surface defects.

Aiming at the motion control problem of two-link manipulator, a new control method based on deep reinforcement learning was proposed. Firstly, the simulation environment of manipulator was built, which includes the two-link manipulator, target and obstacle. Then, according to the target setting, state variables as well as reward and punishment mechanism of the environment model, three kinds of deep reinforcement learning models were established for training. Finally, the motion control of the two-link manipulator was realized. After comparing and analyzing the three proposed models, Deep Deterministic Policy Gradient (DDPG) algorithm was selected for further research to improve its applicability, so as to shorten the debugging time of the manipulator model, and avoided the obstacle to reach the target smoothly. Experimental results show that, the proposed deep reinforcement learning method can effectively control the motion of two-link manipulator, the improved DDPG algorithm control model has the convergence speed increased by two times and the stability after convergence enhances. Compared with the traditional control method, the proposed deep reinforcement learning control method has higher efficiency and stronger applicability.

Existing wireless federated learning frameworks lack the effective support for the actual distributed high-speed Internet of Vehicles (IoV) scenes. Aiming at the distributed learning problem in such scenes, a distributed training algorithm based on the random network topology model named Clustered-Wireless Federated Learning Algorithm (C-WFLA) was proposed. In this algorithm, firstly, a network model was designed on the basis of the distribution situation of vehicles in the highway scene. Secondly, the path fading, Rayleigh fading and other factors during the uplink data transmission of the users were considered. Finally, a wireless federated learning method based on clustered training was designed. The proposed algorithm was used to train and test the handwriting recognition model. The simulation results show that under the situations of good channel state and little user transmit power limit, the loss functions of traditional wireless federated learning algorithm and C-WFLA can converge to similar values under the same training condition, but C-WFLA converges faster; under the situations of poor channel state and much user transmit power limit, C-WFLA can reduce the convergence value of loss function by 10% to 50% compared with the traditional centralized algorithm. It can be seen that C-WFLA is more helpful for model training in high-speed IoV scenes.

A stochastic optimization method was proposed in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet driven by spatio-temporal multi-tasks of terminal delivery. Firstly, the influence of service time and waiting time on the route planning of autonomous vehicle fleet was analyzed to build the shortest route model, and the service sequence network was constructed based on the two-dimensional spatio-temporal network. Then, the vehicle number allocation problem of the minimum autonomous vehicle fleet was converted into a network maximum flow problem through the network transformation, and a minimum fleet model was established with the goal of minimizing the vehicle number of the fleet. Finally, the Dijkstra-Dinic algorithm combining Dijkstra algorithm and Dinic algorithm was designed according to the model features in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet. Simulation experiments were carried out in four different scales of service networks, the results show that:under different successful service rates, the minimum size of autonomous vehicle fleet is positively correlated with the scale of service network, and it decreases with the increase of waiting time and gradually tends to be stable, the One-stop operator introduced into the proposed algorithm greatly improves the search efficiency, and the proposed model and algorithm are suitable for the calculation of the minimum vehicle fleet in large-scale service network.

Information extraction technology can extract the key information in free-text electronic medical records, helping the information management and subsequent information analysis of the hospital. Therefore, the main process of free-text electronic medical record information extraction was simply introduced, the research results of single extraction and joint extraction methods for three most important types of information:named entity, entity assertion and entity relation in the past few years were studied, and the methods, datasets, and final effects of these results were compared and summarized. In addition, an analysis of the features, advantages and disadvantages of several popular new methods, a summarization of commonly used datasets in the field of information extraction of free-text electronic medical records, and an analysis of the current status and research directions of related fields in China was carried out.

When the Log-Structured Merge-tree (LSM-tree) in the time series database is under high write load or resource constraints, file compaction not in time will cause a large accumulation of LSM C ₀ layer data, resulting in an increase in the latency of ad hoc queries of recently written data. To address this problem, a two-stage LSM compaction framework was proposed that realizes low-latency query of newly written time series data on the basis of maintaining efficient query for large blocks of data. Firstly, the file compaction process was divided into two stages:quickly merging of a small number of out-of-order files, merging of a large number of small files, then multiple file compaction strategies were provided in each stage, finally the two-stage compaction resource allocation was performed according to the query load of the system. By implementing the test of the traditional LSM compaction strategy and the two-stage LSM compaction framework on the time series database Apache IoTDB, the results showed that compared with the traditional LSM, the two-stage file compaction module was able to greatly reduce the number of ad hoc query reads while improving the flexibility of the strategy, and made the historical data analysis and query performance improved by about 20%. Experimental results show that the two-stage LSM compaction framework can increase the ad hoc query efficiency of recently written data, and can improve the performance of historical data analysis and query as well as the flexibility of compaction strategy.

In order to improve the real-time ability of obstacle avoidance using image processing technology for quad-rotor helicopter, an obstacle avoidance path planning algorithm was proposed based on Bayesian estimation and region division traversal. Firstly, Bayesian estimation was used to preprocess the video images collected by quad-rotor helicopter. Secondly, obstacle probability analysis was performed to obtain key frames from video images, so as to maximize the real-time performance of the helicopter. Finally, the background difference was carried out on these selected image frames to identify the obstacles, and the pixel point traversal algorithm based on region division was implemented in order to improve the accuracy of obstacle identification. Experimental results show that with the use of the proposed algorithm, the real-time performance of quad-rotor helicopter obstacle avoidance is improved with guaranteeing the obstacle avoidance identification ability, and the maximum distance between the ideal trajectory and the actual flight trajectory of the quad-rotor helicopter is 25.6 cm, while the minimum distance is 0.2 cm. The proposed obstacle avoidance path plan algorithm can provide an efficient solution for quad-rotor helicopter to avoid obstacles by using video images collected by camera.

The various shapes of ferrite magnetic tiles and the wide varieties of their surface defects are great challenges for computer vision based surface defect quality recognition. To address this problem, the deep learning technique was introduced to the magnetic tile surface quality recognition, and a surface defect detection system for magnetic tiles was proposed based on convolution neural networks. Firstly, the tile target was segmented from the collected image and was rotated in order to obtain the standard image. After that, the improved multiscale ResNet18 was used as the backbone network to design the recognition system. During the training process, a novel within-class mixup operation was designed to improve the generalization ability of the system on the samples. To close to the practical application scenes, a surface defect dataset was built with the consideration of illumination changes and posture differences. Experimental results on the self-built dataset indicate that the proposed system achieves recognition accuracy of 97.9%, and provides a feasible idea for the automatic recognition of magnetic tile surface defects.

Concerning the problem that the numerical prediction method is complex in modeling, the radar echo extrapolation method is easy to generate cumulative error and the model parameters are difficult to set in plateau area, a method for segmenting rainfall clouds in Tibet was proposed based on the improved DeepLab v3. Firstly, the convolutional layers and residual modules in the coding network were used for down-sampling. Then, the multi-scale sampling module was constructed by using the dilated convolution, and the attention mechanism module was added to extract deep high-dimensional features. Finally, the deonvolutional layers in the decoding network were used to restore the feature map resolution. The proposed method was compared with Google semantic segmentation network DeepLab v3 and other models on the validation set. The experimental results show that the method has better segmentation performance and generalization ability, has the rainfall cloud segmented more accurately, and the Mean intersection over union (Miou) reached 0.95, which is 15.54 percentage points higher than that of the original DeepLab v3. On small targets and unbalanced datasets, rainfall clouds can be segmented more accurately by this method, so that the proposed method can provide a reference for the rain cloud monitoring and early warning.

The community evolution relationships extracted by the traditional adjacent time slice analysis cannot fully describe the entire community evolution process in dynamic graphs. Therefore, an improved community evolution relationship analysis method was proposed. First, the community events were defined, and the evolution states of the community were described according to the occurred community events. Then, the event matching was performed on two communities within different time slices to obtain community evolution relationships. Results of comparison with the traditional methods show that the total number of community events detected by the proposed method is more than twice that revealed by the traditional method, which proves that the proposed method can provide more useful information for describing the evolution process of communities in dynamic graphs.

To overcome the shortcoming that the Iterative Closest Point (ICP) algorithm is easy to fall into local optimum, an auto-registration method of ground based building point clouds based on line features and ICP algorithm was proposed. Firstly, the plane segmentation was performed on point clouds based on normal consistency. Secondly, the outlines of point clusters were extracted by alpha-shape algorithm, and the feature line segments were obtained by the splitting and fitting process. Then, the feature line pairs were taken as the registration primitives, and the angle and distance between line pairs were used as similarity measures for same-name feature matching in order to achieve the coarse registration of building cloud points. Finally, with the coarse registration result as the initial value, the ICP algorithm was used to realize the fine registration of building point clouds. Two sets of partially overlapping building point clouds were used to carry out the experiments. The experimental results show that the proposed coarse-to-fine registration method can effectively improve the dependency of ICP algorithm on initial value and realize the effective registration of partially overlapping building point clouds.

To solve the problems of interaction difficulty and low efficiency in traditional water flow heating simulation, a method about thermal motion simulation based on Smoothed Particle Hydrodynamics (SPH) was proposed to control the process of water flow heating interactively. Firstly, the continuous water flow was transformed into particles based on the SPH method, the particle group was used to simulate the movement of the water flow, and the particle motion was limited in the container by the collision detection method. Then, the water particles were heated by the heat conduction model of the Dirichlet boundary condition, and the motion state of the particles was updated according to the temperature of the particles in order to simulate the thermal motion of the water flow during the heating process. Finally, the editable system parameters and constraint relationships were defined, and the heating and motion processes of water flow under multiple conditions were simulated by human-computer interaction. Taking the heating simulation of solar water heater as an example, the interactivity and efficiency of the SPH method in solving the heat conduction problem were verified by modifying a few parameters to control the heating work of the water heater, which provides convenience for the applications of interactive water flow heating in other virtual scenes.

The existing deep learning based infrared and visible image fusion methods have too many parameters and require large amounts of computing resources and memory. These methods cannot meet the deployment demand of resource constrained edge devices such as cell phones and embedded devices. In order to address these problems, a light-weight image fusion method based on SqueezeNet was proposed. SqueezeNet was used to extract image features, then the weight map was obtained by these features, and the weighted fusion was performed, finally the fused image was generated. By comparing with the ResNet50 method, it is found that the proposed method compresses the model size and network parameter amount to 1/21 and 1/204 respectively, and improves the running speed to 5 times while maintaining the quality of fused images. The experimental results demonstrate that the proposed method has better fusion effect compared to existing traditional methods as well as reduces the size of fusion model and accelerates the fusion speed.

In order to solve the bottleneck existed in the central controller of centralized service composition, a method of constructing decentralized service composition based on process partitioning was proposed. Firstly, the business process was modeled by the type directed graph. Then, a grouping algorithm was proposed based on the graph transformation method, and the process model was partitioned according to the grouping algorithm. Finally, the decentralized service composition was constructed according to the partitioning results. Test results show that compared with single thread algorithm, the grouping algorithm has the time-consuming for model 1 reduced by 21.4%, and the decentralized service composition constructed has lower response time and higher throughput. The experimental results show that the proposed method can effectively partition the business processes in the service composition, and the constructed decentralized service composition can improve the service performance.

The traditional opinion sentence recognition tasks mainly realize the classification by emotional features inside the sentence. In the task of cross-lingual multi-document opinion sentence recognition, the certain supporting function for opinion sentence recognition was provided by the association between sentences in different languages and documents. Therefore, a Chinese-Vietnamese bilingual multi-document news opinion sentence recognition method was proposed by combining Bi-directional Long Short Term Memory (Bi-LSTM) network framework and sentence association features. Firstly, emotional elements and event elements were extracted from the Chinese-Vietnamese bilingual sentences to construct the sentence association diagram, and the sentence association features were obtained by using TextRank algorithm. Secondly, the Chinese and Vietnamese news texts were encoded in the same semantic space based on the bilingual word embedding and Bi-LSTM. Finally, the opinion sentence recognition was realized by jointly considering the sentence coding features and semantic features. The theoretical analysis and simulation results show that integrating sentence association diagram can effectively improve the precision of multi-document opinion sentence recognition.

The channel estimation error seriously affects the key generation consistency of two communicating parties in the process of wireless key generation, a multi-bit quantization wireless key generation scheme under imperfect channel estimation condition was proposed. Firstly, in order to investigate the influence of imperfect channel estimation on wireless key generation, a channel estimation error model was established. Then, a multi-bit key quantizer with guard band was designed, and the performance of wireless key was able to be improved by optimizing the quantization parameters. The closed-form expressions of Key Disagreement Rate (KDR) and Effective Key Generation Rate (EKGR) were derived, and the relationships between pilot signal power, quantization order, guard bands and the above two key generation performance indicators were revealed. The simulation results show that, increasing the transmit pilot power can effectively reduce the KDR, and with the increasing of quantization order, the key generation rate can be improved, but the KDR also increases. Moreover, increasing the quantization order and choosing the appropriate guard band size at the same time can effectively reduce KDR.

For the Flexible Job-shop Scheduling Problem (FJSP) with machine flexibility and machine tool depreciation, in order to reduce the energy consumption in the production process, a mathematical model with the minimization of weighted sum of maximum completion time and total energy consumption as the scheduling objective was established, and an Improved Genetic Algorithm (IGA) was proposed. Firstly, according to strong randomness of Genetic Algorithm (GA), the principle of balanced dispersion of orthogonal test was introduced to generate initial population, which was used to improve the search performance in global range. Secondly, in order to overcome genetic conflict after crossover operation, the coding mode of three-dimensional real numbers and the arithmetic crossover of double individuals were used for chromosome crossover, which reduced the steps of conflict detection and improved the solving speed. Finally, the dynamic step length was adopted to perform genetic mutation in mutation operation stage, which guaranteed local search ability in global range. By testing on the 8 Brandimarte examples and comparing with 3 improved heuristic algorithms in recent years, the calculation results show that the proposed algorithm is effective and feasible to solve the FJSP.

Movidius neural computing stick is a USB-based deep learning inference tool and a stand-alone artificial intelligence accelerator that provides dedicated deep neural network acceleration for a wide range of mobile and embedded vision devices. For the embedded application of deep learning, a near real-time pedestrian target detection method based on Movidius neural computing stick was realized. Firstly, the model size and calculation were adapted to the requirements of the embedded device by improving the RefineDet target detection network structure. Then, the model was retrained on the pedestrian detection dataset and deployed on the Raspberry Pi equipped with Movidius neural computing stick. Finally, the model was tested in the actual environment, and the algorithm achieved an average processing speed of 4 frames per second. Experimental results show that based on Movidius neural computing stick, the near real-time pedestrian detection task can be completed on the Raspberry Pi with limited computing resources.

Administrators usually take some network security metrics as important bases to measure network security. Common Vulnerability Scoring System (CVSS) is one of the generally accepted network measurement method. Aiming at the problem that the existing network security measurement based on CVSS could not accurately measure the probability and the impact of network attack at the same time, an improved base metric algorithm based on dependency relationship graph and CVSS was proposed. Firstly, the dependency relationship of the vulnerability nodes in an attack graph was explored to build the dependency relationship graph. Then, the base metric algorithm of the vulnerability in CVSS was modified according to the dependency relationship. Finally, the vulnerability scores in the whole attack graph were aggregated to obtain the probability and the impact of network attack. The results of simulation with simulated attacker show that the proposed algorithm is superior to the algorithm of aggregating CVSS scores in terms of accuracy and credibility, and can get measurement results closer to the actual simulation results.

With the rapid development of edge intelligence, the development of existing handwritten numeral recognition convolutional network models has become less and less suitable for the requirements of edge deployment and computing power declining, and there are problems such as poor generalization ability of small samples and high network training costs. Drawing on the classic structure of Convolutional Neural Network (CNN), Leaky_ReLU algorithm, dropout algorithm, genetic algorithm and adaptive and mixed pooling ideas, a handwritten numeral recognition model based on LeNet-DL improved convolutional neural network was constructed. The proposed model was compared on large sample MNIST dataset and small sample REAL dataset with LeNet, LeNet+sigmoid, AlexNet and other algorithms. The improved network has the large sample identification accuracy up to 99.34%, with the performance improvement of about 0.83%, and the small sample recognition accuracy up to 78.89%, with the performance improvement of about 8.34%. The experimental results show that compared with traditional CNN, LeNet-DL network has lower training cost, better performance and stronger model generalization ability on large sample and small sample datasets.

The OpenPose model originally used for the detection of key points in human skeleton can greatly shorten the detection cycle while maintaining the accuracy of the Regional Multi-Person Pose Estimation (RMPE) model and the Mask Region-based Convolutional Neural Network (R-CNN) model, which were proposed in 2017 and had the near-optimal detection effect at that time. At the same time, the OpenPose model has the problems such as low parameter sharing rate, high redundancy, long time-consuming and too large model scale. In order to solve the problems, a new OpenPose-slim model was proposed. In the proposed model, the network width was reduced, the number of convolution block layers was decreased, the original parallel structure was changed into sequential structure and the Dense connection mechanism was added to the inner module. The processing process was mainly divided into three modules:1) the position coordinates of human skeleton key points were detected in the key point localization module; 2) the key point positions were connected to the limb in the key point association module; 3) limb matching was performed to obtain the contour of human body in the limb matching module. There is a close correlation between processing stages. The experimental results on the MPII dataset, Common Objects in COntext (COCO) dataset and AI Challenger dataset show that, the use of four localization modules and two association modules as well as the use of Dense connection mechanism inside each module of the proposed model is the best structure. Compared with the OpenPose model, the test cycle of the proposed model is shortened to nearly 1/6, the parameter size is reduced by nearly 50%, and the model size is reduced to nearly 1/27.

Aiming at the problems of insufficient use of semantic information and the poor summarization precision in the current generated text summarization method, a text summarization method was proposed based on dual encoder. Firstly, the dual encoder was used to provide richer semantic information for Sequence to Sequence (Seq2Seq) architecture. And the attention mechanism with dual channel semantics and the decoder with empirical distribution were optimized. Then, position embedding and word embedding were merged in word embedding technology, and Term Frequency-Inverse Document Frequency (TF-IDF), Part Of Speech (POS), key Score (Soc) were added to word embedding, as a result, the word embedding dimension was optimized. The proposed method aims to optimize the traditional sequence mapping of Seq2Seq and word feature representation, enhance the model's semantic understanding, and improve the quality of the summarization. The experimental results show that the proposed method has the performance improved in the Rouge evaluation system by 10 to 13 percentage points compared with traditional Recurrent Neural Network method with attention (RNN+atten) and Multi-layer Bidirectional Recurrent Neural Network method with attention (Bi-MulRNN+atten). It can be seen that the proposed method has more accurate semantic understanding of text summarization and the generation effect better, and has a better application prospect.

In complex environment, existing pedestrian detection methods can not be very good to achieve high recall rate and efficient detection. To solve this problem, a pedestrian detection method based on Convolutional Neural Network (CNN) was proposed. Firstly, pedestrian locations in input images were initially detected with single step detection upgrade network (YOLOv2) derived from CNN. Secondly, a network with target classification and bounding box regression was designed to cascade with YOLOv2 network, which made reclassification and regression of pedestrian location initially detected by YOLOv2, to reduce error detections and increase recall rate. Finally, a Non-Maximum Suppression (NMS) method was used to remove redundant bounding boxes. The experimental results show that, in INRIA and Caltech dataset, the proposed method increases recall rate by 3.3 percentage points, and the accuracy is increased by 5.1 percentage points compared with original YOLOv2. It also reached a speed of 11.6FPS (Frames Per Second) to realize real-time detection. Compared with the existing six popular pedestrian detection methods, the proposed method has better overall performance.

For the distortion of the image after embedding secret is excessively obvious, a new Reversible Data Hiding (RDH) based on Pixel Value Order (PVO) was proposed. Firstly, the pixels of a carrier image were divided into gray and white layers, the pixels of a gray layer were selected as the target pixels, and the four white pixels in the cross positions of the target pixels were sorted. Secondly, according to the sorting result, the mean value of the two end pixels and the mean value of the median pixels were calculated, and the reversible constraint was used to achieve dynamic prediction of pixels. Finally, a Prediction Error Histogram (PEH) was constructed according to the prediction result. Six images in the USC-SIPI standard image library were used for simulation experiments. The experimental results show that, when the Embedding Capacity (EC) is 10000 b and the average Peak Signal-to-Noise Ratio (PSNR) is 61.89 dB, the proposed algorithm can effectively reduce the distortion of the image with ciphertext.

The semi-automatic road extraction method needs more artificial participation and is time-consuming, and its accuracy of road extraction is low. In order to solve the problems, a new method of road extraction from multi-source high resolution remote sensing image based on Fully Convolutional neural Network (FCN) was proposed. Firstly, the GF-2 and World View high resolution remote sensing images were divided into small pieces, the images containing roads were classified by Convolutional Neural Network (CNN). Then, the Canny operator was used to extract the edge feature information of road. Finally, RGB, Gray and ground truth were combined and put into the FCN model for training, and the existing FCN model was extended to a new FCN model with multi-satellite source input and multi-feature source input. The Shigatse region of Tibet was chosen as the research area. The experimental results show that, the proposed method can achieve the extraction precision of 99.2% in the road extraction from high resolution remote sensing images, and effectively reduce the time needed for extraction.

In order to solve the problem of high cost of chip testing in the process of Three-Dimensional (3D) chip manufacturing, a new scheduling method based on Time Division Multiplexing (TDM) was proposed to optimize the testing resources between layers, layer and core cooperatively. Firstly, the shift registers were arranged on each layer of 3D chip, and the testing frequency was divided properly between the layers and cores of the same layer under the control of shift register group on input data, so that the cores in different locations could be tested in parallel. Secondly, greedy algorithm was used to optimize the allocation of registers for reducing the free test cycles of core parallel test. Finally, Discrete Binary Particle Swarm Optimization (DBPSO) algorithm was used to find out the best 3D stack layout, so that the transmission potential of the Through Silicon Via (TSV) could be adequately used to improve the parallel testing efficiency and reduce the testing time. The experimental results show that, under the power constraints, the utilization rate of the optimized whole Test Access Mechanism (TAM) is increased by an average of 16.28%, and the testing time of the optimized 3D stack is reduced by an average of 13.98%. The proposed method can decrease the time and reduce the cost of testing.

In terms of the evolution of complex dynamics in the dissemination of public opinion, a dynamic evolution model was proposed based on transmission dynamics. Firstly, the models of public opinion and its evolution were constructed and the static solution was obtained through equation transformation. Secondly, the Fokker-Planck equation was introduced to analyze the asymptotic behavior of public opinion evolution, getting the steady-state solution and solving it. In that case, the correlation between the complex network and the model was built and the experiment objective of simulation research was put forward. Finally, through the simulation analysis of the public opinion evolution model and the public opinion model with the Fokker-Planck equation, and the empirical analysis of real micro-blog public opinion data, the essence of the dissemination and evolution of public opinion in the complex network was studied. The results show that the asymptotic behavior of public opinion network evolution is consistent with the distribution of degrees and the connection way of network public opinion dissemination is influenced by nodes. The model can describe the dynamic behavior in the formation and evolution of micro-blog public opinion network.

Due to the high complexity of intra encoding in High Efficiency Video Coding (HEVC), an efficient intra encoding algorithm combining coding unit segmentation and intra mode selection based on texture feature was proposed. The strength of dominant direction of each depth layer was used to decide whether the Coding Unit (CU) need segmentation, and to reduce the number of intra modes. Firstly, the variance of pixels was used in the coding unit, and the strength of dominant direction based on pixel units to was calculated determine its texture direction complexity, and the final depth was derived by means of the strategy of threshold. Secondly, the relation of vertical complexity and horizontal complexity and the probability of selected intra model were used to choose a subset of prediction modes, and the encoding complexity was further reduced. Compared to HM15.0, the proposed algorithm saves 51.997% encoding time on average, while the Bjontegaard Delta Peak Signal-to-Noise Rate (BDPSNR) only decreases by 0.059 dB and the Bjontegaard Delta Bit Rate (BDBR) increases by 1.018%. The experimental results show that the method can reduce the encoding complexity in the premise of negligible RD performance loss, which is beneficial to real-time video applications of HEVC standard.

Aiming at enhancing the embedded capacity and enriching the available encryption algorithm of reversible data hiding in encrypted domain, a new scheme was proposed by adopting double-level encryption methods and embedding the secret information based on Code Division Multiple Access (CDMA). The image was first divided into blocks and a multi-granularity encryption was introduced. The image was first divided into blocks, which were scrambled by introducing multi-granularity encryption, then 2 bits in the middle of each pixel in blocks were encrypted by a stream cipher. Based on the idea of CDMA, k mutually orthogonal matrices of 4 bits were selected to carry k-level secret information. The orthogonal matrices can guarantee the multi-level embedding and improve the embedding capacity. The pseudo bit was embedded into the blocks that cannot meet the embedding condition. The secret data could be extracted by using the extraction key; the original image could be approximately recovered by using the image decryption key; with both of the keys, the original image could be recovered losslessly. Experimental results show that, when the Peak Signal-to-Noise Ratio (PSNR) of gray Lena image of 512×512 pixels is higher than 36 dB, the maximum embedded capacity of the proposed scheme is 133313 bit. The proposed scheme improves the security of encrypted images and greatly enhances the embedded capacity of reversible information in ciphertext domain while ensuring the reversibility.

To solve the problem of color gradient filling of complicated shape in its stretching trend, a Shape Main Skeleton Color Gradient Filling Algorithm (SMSCGFA) was proposed by using shape main skeleton. Based on visual salience estimating vector, the main skeleton was extracted from a shape by using whole selecting and local geometric optimization. Some important methods in SMSCGFA were studied that included extracting skeleton using Constrained Delaunay Triangulation (CDT), and extracting skeleton path by double stacks. Gradient filling color in main skeleton was computed, and the whole shape gradient filling color was completed by local main skeleton color filling information. The experiment results show that the optimized skeleton path ratio reduces to 5.5%, and more skeletons with redundant branches are eliminated, and SMSCGFA satisfies subjective visual perception in shape stretching trend compared with color linear filling.