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This paper presents an in-depth study and analysis of X-ray inspection of basin insulators by wireless sensing technology. Aiming at the characteristics of low contrast and many kinds of noise in the basin insulator ray image, this paper proposes an X-ray basin insulator image denoising method based on improved 3D block matching. Using the RF microcontroller CC2530 chip as the core hardware and networked by ZigBee protocol, the sensor senses and collects various parameters and transmits this information to the monitoring end in real time through wireless. The method proposes an improved wavelet thresholding denoising method to overcome the pseudo-Gibbs phenomenon caused by the wavelet hard thresholding method in the 3D block matching algorithm cofiltering and retain more details of the image. Aiming at the ringing effect caused by the Wiener filtering method used in the three-dimensional block matching algorithm collaborative filtering, an improved Kalman filtering method based on anisotropic diffusion is proposed, which avoids the ringing effect, and has clear edges and complete details. An improved Kalman filtering method based on anisotropic diffusion is proposed to avoid the ringing effect, and the edges are clear, and the details are complete. The proposed method is a safe, efficient, accurate, and feasible method for detecting defects in basin insulators by combining X-ray and improved wireless image sensing technology to detect the internal equipment without disassembling or touching the GIS equipment.

Digital video technology has been increasingly needed in various fields, such as telecommunications, entertainment, medicine. Therefore, video compression is required. Motion estimation methods help in improving video compression efficiency by effectively removing the temporal redundancy between successive frames. Several block-based motion estimation (BME) algorithms are being suggested to reduce the coding process’s computational complexity. This paper proposes a new rapid hybrid (BME) algorithm established on the primary search point prediction and advance ending search point strategies. It combines rough adaptive search and effective local search. The coarse search introduces a new motion vector (MV) prediction technique that utilizes the macro-blocks (MBs) Spatio-temporal correlations to optimize the traditional adaptive-rood-pattern search algorithm (ARPS) and speeding up the whole process without affecting the accuracy. In the accurate local search, the cross-formed search pattern using a one-step search (OSS) block matching algorithm is employed, to estimate the actual (MV) with less computation time and further speed up the search efficiency. Exhaustive experiments are performed to demonstrate the present algorithm’s performance over the benchmark schemes concerning specific assessment criteria for results, including the peak signal-to-noise ratio (PSNR), computational complexity and computational gain. The results show that the proposed algorithm is efficient and reliable; it can always give better performance over diamond search (DS) and (ARPS). The conducted test shows an increased performance of search speed while preserving the visual quality of the motion-compensated images, and it achieves 59.76–88.03 speed improvement over (DS) and 20.98–72.06 over (ARPS) for different video sequences. Besides, the suggested method (ARP-OSS) provides the best result compared to (DS) and (ARPS) in terms of time complexity for analyzing all the video samples.

Event cameras are bio-inspired sensors that capture intensity changes of pixels individually, and generate asynchronous and independent “events”. Due to the fundamental difference from the conventional cameras, most research on event cameras builds a global event frame by grouping events according to their timestamps or their number to employ traditional computer vision algorithms. However, in order to take advantage of event cameras, it makes sense to generate asynchronous output on an event-by-event basis. In this paper, we propose an optical flow estimation algorithm with low latency and robustness to various scenes to utilize the advantage of the event camera by enhancing the existing optical flow algorithm. Furthermore, we estimate angular velocity with low latency using the proposed optical flow stream. For the validation of algorithms, we evaluate the accuracy and latency of optical flow with publicly available datasets. Moreover, we assess the performance of the proposed angular velocity estimation in comparison to the existing algorithms. Both validations suggest that our asynchronous optical flow shows comparable accuracy to the existing algorithms and the latency is reduced by half compared to the existing block matching algorithm on average. Also, our angular velocity estimation is superior to the existing algorithms in terms of accuracy and robustness while showing low latency within 15 ms consistently.

Purpose The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 × 10 and 20 × 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates “no parking space for that place.” Otherwise, the method yields “parking place available.” Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.

Complex object illumination transfer is a special challenge in computer vision. In our paper, we put forward a method for complex object illumination transfer. Firstly, the input object image was divided into object components by semantic analysis, to find the reference object images consistent with the object component material in the physical world by material analysis. Material has a great influence on the illumination transfer of object image, so the use of material analysis can greatly reduce the influence of material on the illumination transfer in the later stage. Next, a block matching algorithm was used to deform each reference object image and made it match with each component shape of the input object image. Then, each component of the input object image and each warped reference object image were illuminated by local and global transfer model. Finally, semantic analysis was used to synthesize the re-illumination components of the input object image to obtain the re-illumination input object image. The experimental results prove that the method could make a good effect on the illumination transfer. Our main contribution is the use of semantic and material analysis to split complex objects into simple objects, and skillfully combine semantic and material parsing and composition, block matching algorithm, local and global light migration model to achieve the relighting of complex objects.

Augmented reality can enhance people’s perception of the environment by embedding virtual objects or other information in real-time images. In this paper, the color image is used as a reference to calculate the confidence of the original depth map, and stereo matching is performed according to the feature points. The depth map is mainly enhanced by the color, edge, and segmentation results of the color image. A deep computing system based on augmented reality is designed. The system can use a binocular camera to collect object images in real time and obtain better parallax images by correcting the calibrated image. The semi-global block matching algorithm and depth calculation are used to realize the tracking registration of virtual objects. Experiments in different environments show that the system has good real-time performance, light invariance, and depth consistency.

Speckle tracking is the most prominent technique used to estimate the regional movement of the heart based on echocardiograms. In this study, we propose an optimised-based block matching algorithm to perform speckle tracking iteratively. The proposed technique was evaluated using a publicly available synthetic echocardiographic dataset with known ground-truth from several major vendors and for healthy/ischaemic cases. The results were compared with the results from the classic (standard) two-dimensional block matching. The proposed method presented an average displacement error of 0.57 pixels, while classic block matching provided an average error of 1.15 pixels. When estimating the segmental/regional longitudinal strain in healthy cases, the proposed method, with an average of 0.32 ± 0.53, outperformed the classic counterpart, with an average of 3.43 ± 2.84. A similar superior performance was observed in ischaemic cases. This method does not require any additional ad hoc filtering process. Therefore, it can potentially help to reduce the variability in the strain measurements caused by various post-processing techniques applied by different implementations of the speckle tracking. Graphical Abstract Standard block matching versus proposed iterative block matching approach.

A stereoscopic image consists of two views rendering a depth sense. Indeed each eye is constrained to look at one view, and the small objects displacements across the two views are interpreted as an indication of depth. These displacements are exploited as specific inter-view redundancies from a compression viewpoint. The classical still compression scheme, called disparity-compensated compression scheme, compresses one view independently of the second view, and a block-based disparity map modeling the displacements is losslessly compressed. The difference between the original view and its disparity predicted view is then compressed and used by the decoder to compute the compensated view to improve the disparity predicted view. However, a proof of concept work has already shown that selecting disparities according to the compensated view, instead of the predicted view, yields increased rate-distortion performance. This paper derives from the JPEG-coder, a disparity-dependent analytic expression of the distortion induced by the compensated view. This expression is embedded into an algorithm with a reasonable numerical complexity approaching the performance obtained with the proof of concept work. The proposed algorithm, called fast disparity-compensated block matching algorithm, provides at the same bitrate an average performance increase as compared to the classical stereoscopic image coding schemes.

A point cloud visualizes information by placing a voxel with a color value and a position value in a three-dimensional space. Since a point cloud uses hundreds of thousands or millions of points to visualize information, a large number of bits is needed compared to existing 2D media. Therefore, it is essential to compress point data for transmission and storage. The Moving Picture Expert Group (MPEG) is developing a point cloud compression method based on 2D video that takes advantage of the benefits of coding efficiency and the wide adaption of video codecs by various industries. This compression method is called video-based point cloud compression (V-PCC). Generally, video codecs use a compression method that employs a block matching algorithm. Currently, V-PCC is conducted using 2D video codecs, which means that motion information used by V-PCC is obtained from 2D video sequences. Thus, this 2D-based motion information limits the characterization of the motion in terms of 3D-points, which is also disadvantageous to compression efficiency. In this paper, we propose a method for estimating and compensating the motion in terms of a 3D object when compressing a dynamic object point cloud using a conventional video codec. The proposed 3D motion estimation and compensation technology showed higher gain overall in terms of BD-rate and was proven to effectively compress 3D point cloud content on the basis of 3D motion.

Capturing large fields of view with only one camera is an important aspect in surveillance and automotive applications, but the wide-angle fisheye imagery thus obtained exhibits very special characteristics that may not be very well suited for typical image and video processing methods such as motion estimation. This paper introduces a motion estimation method that adapts to the typical radial characteristics of fisheye video sequences by making use of an equisolid re-projection after moving part of the motion vector search into the perspective domain via a corresponding back-projection. By combining this approach with conventional translational motion estimation and compensation, average gains in luminance PSNR of up to 1.14 dB are achieved for synthetic fish-eye sequences and up to 0.96 dB for real-world data. Maximum gains for selected frame pairs amount to 2.40 dB and 1.39 dB for synthetic and real-world data, respectively.

Cardiac strain imaging (CSI) using ultrasound radiofrequency (RF) data is more sensitive to subtle myocardial motion abnormalities compared to echocardiographic measurements and envelope-based speckle tracking. Yet, CSI is being actively researched to address challenges from out-of-plane scatter motion due to complex 3D cardiac deformation imaged in 2D. Here, we report on a feasibility study to apply adaptive Bayesian CSI for in vivo apical infarct localization using 2D ultrasound RF data from murine models of ischemia-reperfusion (IR) and myocardial infarction (MI). High frequency ( $\mathrm{f}_{\mathrm{c}}=30\text{MHz}$ ) ultrasound (US) radio-frequency data collected in parasternal short axis view at the apical level were tracked using an adaptive Bayesian regularization incorporated multi-level block matching algorithm. In vivo longitudinal study was designed with five imaging sessions (pre-surgery (BL) and 1,2,7 and 14 days post-surgery). End-systole circumferential strain images and values were compared among three different mouse models - sham, myocardial infarction, and ischemia-reperfusion. Findings from cardiac strain imaging demonstrated good correlation with the findings from ex vivo histopathological image analysis thus showing the feasibility of the proposed method.

The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.

Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity. This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search.

Variable block size motion estimation has contributed greatly to achieving an optimal interframe encoding, but involves high computational complexity and huge memory access, which is the most critical bottleneck in ultra-high-definition video encoding. This article presents a hardware-efficient block matching algorithm with an efficient hardware design that is able to reduce the computational complexity of motion estimation while providing a sustained and steady coding performance for high-quality video encoding. A three-level memory organization is proposed to reduce memory bandwidth requirement while supporting a predictive common search window. By applying multiple search strategies and early termination, the proposed design provides 1.8 to 3.7 times higher hardware efficiency than other works. Furthermore, on-chip memory has been reduced by 96.5% and off-chip bandwidth requirement has been reduced by 39.4% thanks to the proposed three-level memory organization. The corresponding power consumption is only 198mW at the highest working frequency of 500MHz. The proposed design is attractive for high-quality video encoding in real-time applications with low power consumption.

Video compression is a very efficient method for storage and transmission of digital video signal. The applications include multimedia transmission, teleconferencing, videophone, high-definition television (HDTV), CD-ROM storages, etc. The hybrid coding techniques based on predictive and transform coding are the most popular and adopted by many video coding standards such as MPEG-1/2/4 [1] and H.261/H.263/H.264 [2, 3], owing to its high compression efficiency. In the hybrid coding system, the motion compensation, first proposed by Netravali and Robbins in 1997, plays a key role from the view point of coding efficiency and implementation cost [4-11]. A generic hybrid video coder is depicted in Figure 1. Fig. 1. A generic hybrid motion compensated DCT video coder. DCT Quan-tization De-Quan-tization IDCT Motion Compensation Motion Estimation Frame Buffer Variable Length Coder

This paper attempts to improve denoising efficiency of BM3D technique for videos, i.e., VBM3D. VBM3D constructs 3D cubes from target video frames by a block matching algorithm that minimizes the residual matching error. However, such a cube formation results in sacrificing the pixel correlation in the temporal direction. This paper thus modifies this step to preserve the sub-pixel alignment, which makes the Fourier coefficients of each cube located on a vicinity of a certain plane in the 3-D Fourier domain. Then, SURE-shrinkage technique is separately applied to the inside and outside of the vicinity of the plane to denoise each cube. The experimental results given in this paper demonstrate the validity of our approach.

The modern era of digital communication heavily relied on video for information, communication, and education. Videos are becoming an integral part of the digital world. The tremendous use of videos leads to the production of a high amount of data sometimes referred to as Big Data. So to process this much data with limited resources is impossible if videos are not compressed properly and efficiently. The heart of video compression is the process that predicts the movement of an object from one frame to another by using different Block Matching Algorithms and is called motion estimation. Many Block Matching Algorithms were reported to carry out the motion estimation to date but none of the research work considered the direction of movement of Macro Blocks i.e. objects while applying any algorithm. The proposed work titled Predictive Rood Pattern Search (PRPS), here will first identify the direction of movement of the object and apply the best suitable algorithms out of the existing but best among the reported works which consider the correlation between the current macroblock and the neighboring macroblock. The obtained results show the efficiency of the new algorithms over the other algorithms while providing the option of selecting the proper algorithm based on direction.

The challenge of motion estimation (ME) process in the video compression (VC) technique is to balance the complexity, visual experience, and speed, which directly affects the efficiency of video compression. An abundant number of block matching algorithms (BMA) was designed only to achieve the above objective of ME. This paper presents a dynamic version of existing BMAs for ME, which uses the ideas and concepts of the three most effective BMAs, namely reduced three-step logarithmic search (RTSLS), simple and efficient search (SES), and adaptive rood pattern search (ARPS) reported till date. The proposed algorithm yields equivalent visual experience than that of full search (FS) but is several times faster than FS.

Motion estimation is the cornerstone of main video compression standards, which are based on the reduction of the temporal redundancy between consecutive frames. Although the mechanism is simple, the best method, Full Search, uses a brute-force approach which is not suited for real-time applications. This work introduces a high performance architecture for performing on-the-fly full-search block matching estimation in FPGA devices, which has been modeled using C++ programming language and synthesized with Vivado HLS for a Xilinx ZC706 prototyping board. The architecture is based on a dataflow datapath and it is configurable, enabling a fast and easy exploration of the solution space. On-board results achieve a maximum performance of 743 fps, 247 fps and 110 fps for VGA, HD and FHD video resolutions, respectively, for a typical macroblock size of \(16 \times 16\) pixels and a search area of \(\pm 16\) pixels.

Of all the motion estimation approaches, block matching approach is the most powerful and effective method for motion estimation of video sequences. In recent years nature-inspired algorithms are being used in an effective way for motion estimation. This study proposes a novel approach of motion estimation: differential evolution-based block matching algorithm for motion estimation. To showcase the effectiveness of the proposed work, we have used four well-known test video sequences. The video sequences used in the experiments have all the required characteristics like diverse resolutions, formats, and the frame count that are needed in input video sequences. The proposed approach is compared with standard block matching algorithms by considering the parameters like structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). The empirical results indicate that our proposed algorithms perform better in comparison to standard block matching algorithms.

The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.

International audience; 3D ultrasound imaging can be used in the context of robotic needle steering to reach a physical target with a flexible, steerable needle. During the insertion, the tissue may be deformed by the inserted needle, patient breathing or external force application. It may therefore be necessary to track intra-operatively the displacement of the target. Most ultrasound based needle steering works concentrate on 2D ultrasound probes [1], [2], [3] which do not allow to simultaneously track both the target and the needle during 3D needle steering. Physical target tracking in 3D ultrasound-guided needle steering is seldom carried out [4][5], and may require computational power that is precious for intra-operative needle steering. This paper proposes a new approach for computationally inexpensive and precise tracking of a moving target in 3D B-mode ultrasound volumes. It is based on the interconnection of intensity-based tracking and motion estimation algorithms. The intensity-based tracking consists in a 3D extension of the Diamond Shape block matching algorithm, used here for the first time in 3D ultrasound volumes for tissue tracking. The motion estimation is done by linear Kalman filtering. It predicts the next target position and ensures faster and more robust convergence of the Diamond Shape block matching algorithm. An experimental validation on ex-vivo tissue is proposed with promising tracking precision (estimated average error of 0.3mm) while significantly lowering the computational cost when compared to classical block matching based tracking.

Object tracking and motion estimation are key components in large number of applications, ranging from the navigation of autonomous vehicles to video data compression. This object tracking uses the motion estimation for continuous tracking of distinctive features in a successive manner. This helps to detect the motion of features used in computing to detect the exact or appropriate solution by optimizing the block size. To obtain the better prediction quality by using variable size motion estimation, proposed a block matching algorithm for motion estimation, object tracking, and video data compression.

Nowadays, the requirement of quality videos can increase due to the growth of video coding applications, but most applications affected by motion estimation. Recently block matching (BM) algorithms performed very effective and simple for implementation than other motion estimations approach. Different BM architectures are used to estimate the motion with real-time speed but not in quality factors. In this paper, we concentrate to enhance both metrics are speed and quality. The hardware design of BM algorithm is implemented using the modified differential evolution optimization (MDEO), which provides the quality requirement without affecting the area, power, and delay. The main objective of our MDEO-BM algorithm is to computes the motion vector as faster manner without compromising quality. Hardware structure of our MDEO-BM algorithm implemented in Verilog language using Virtex-6 FPGA family and synthesized using Xilinx ISE Design Suite 14.5. The proposed architecture of MDEO-BM algorithm achieves a critical path delay of 4.487 ns and the maximum frequency of 222.875 MHz. The performance of proposed architecture is analyzed by delay, area, and power and quality of estimated motion vectors are analyzed by PSNR with different test video sequences. Simulation results shows that our MDEO-BM algorithm performs efficient in terms of hardware utilization, power consumption, maximum clock frequency and quality metrics.

Block matching algorithm is the most popular motion estimation technique, due to its simplicity of implementation and effectiveness. However, the algorithm suffers from a long computation time which affects its general performance. In order to achieve faster motion estimation, a new block matching algorithm based on stochastic fractal search, SFS, is proposed in this paper. SFS is a metaheuristic technique used to solve hard optimization problems in minimal time. In this work, two main contributions are presented. The first one consists of computing the motion vectors in a parallel structure as opposed to the other hierarchical metaheuristic block matching algorithms. When the video sequence frame is divided into blocks, a multi-population model of SFS is used to estimate the motion vectors of all blocks simultaneously. As a second contribution, the proposed algorithm is modified in order to enhance the results. In this modified version, four ideas are investigated. The random initialization, usually used in metaheuristics, is replaced by a fixed pattern. The initialized solutions are evaluated using a new fitness function that combines two matching criteria. The considered search space is controlled by a new adaptive window size strategy. A modified version of the fitness approximation method, which is known to reduce computation time but causes some degradation in the estimation accuracy, is proposed to balance between computation time and estimation accuracy. These ideas are evaluated in nine video sequences and the percentage improvement of each idea, in terms of estimation accuracy and computational complexity, is reported. The presented algorithms are then compared with other well-known block matching algorithms. The experimental results indicate that the proposed ideas improve the block matching performance, and show that the proposed algorithm outperforms many state-of-the-art methods.

Motion estimation is one of the most crucial and time-consuming component of video compression methods. However, much research has been done to improve computational complexity at the expense of the loss in performance of matching of blocks. A novel block matching algorithm named efficient direction-oriented search is proposed. For this, the proposed algorithm firstly aims to dynamically switch between search regions based on the location of minimum distortion error. The search region dimension is also made adaptive for faster convergence. Then the computational complexity is reduced by using a proposed horizontal, vertical wings diamond search pattern and, two ± 45 ∘ inclined hexagon-shaped direction-oriented search patterns. For further speed-up in the search process, partial distortion calculations are employed. A method for optimal threshold value selection based on the distortion statistics for different partial distortion calculations is presented. The performance of the proposed algorithm is evaluated for different video sequences containing: slow, medium, fast, and directional motion content. The experimental results indicate that significant improvement in speed-up can be achieved while maintaining the better peak signal-to-noise-ratio performance. For directional motion video sequences, the proposed method even outperforms the full search algorithm with a significantly lower computational cost.

In copy-and-paste image forgeries, where image content is copied from one image and pasted into another, inconsistencies in an imaging feature called lateral chromatic aberration (LCA) are intrinsically introduced. In this paper, we propose a new methodology to detect forged image regions that is based on detecting localized LCA inconsistencies. To do this, we propose a statistical model that captures the inconsistency between global and local estimates of LCA. We then use this model to pose forgery detection as a hypothesis testing problem and derive a detection statistic, which we show is optimal when certain conditions are met. To test its detection efficacy, we conduct a series of experiments that demonstrate our proposed methodology significantly outperforms prior art and addresses deficiencies of previous research. Additionally, we propose a new and efficient LCA estimation algorithm. To accomplish this we adapt a block matching algorithm, called diamond search, which efficiently measures the LCA in a localized region. We experimentally show that our proposed estimation algorithm reduces estimation time by two orders of magnitude without introducing additional estimation error.

Motion estimation (ME) plays an important part in the functioning of the video codec by identifying and reducing the temporal redundancies in between successive frames of a video sequence. Block matching algorithm (BMA) has been accepted as one of the finest approaches for motion estimation due to its efficiency and ease of implementation. This paper presents a new and improved iterative and adaptive search strategy for block-based motion estimation along with its efficient hardware implementation. Since it is expected that there will be more demand for streaming video services on mobile devices, designing fine tuning algorithm with dedicated efficient hardware would provide significant benefits. The present motion estimation algorithm is adaptive in nature that takes into consideration the motion content of the current frame while predicting the motion vector. The adaptive nature of the search eases the complexity of motion estimation and the algorithm makes use of the correlation present among the motion vectors of the neighboring blocks to lower the number of search position. Traditionally, such adaptive algorithms are executed by CPU cores running a software stack. Since software involves a significant amount of overheads like fetching into cache, branches, stalls etc., the efficiency of the proposed algorithm can be overshadowed by the hardware platform. To avoid this, compact hardware architecture was developed which stands ahead of other existing architectures as shown in comparison. The VLSI design for the proposed algorithm presented in this work deals with the generation of the adaptive search pattern and use of interleaved memory organization fasten the operational speed. A profitable data re-use scheme and involvement of minimum processing elements required for parallelization reduce the on-chip area. Working at a frequency of 243 MHz, the proposed design can process 66 720p HD (1280 × 720) frames in one second consuming an area of 38.2 K gate equivalent. Hence, the proposed design can be incorporated in video codecs to be used in commercial devices like camcorders, smart phones and other portable, battery-powered video consumer devices. The proposed research method achieves significantly improved results both in terms of algorithmic metrics (PSNR) as well hardware performance (speed, area).

Parametric perfusion imaging (PPI) based on dynamic contrast-enhanced ultrasound (DCEUS) is a multi-parametric functional method that is increasingly used to characterize the hemodynamic features of abdominal tumors. Periodic respiratory kinetics adversely affects the signal-to-clutter ratio (SCR) and accuracy of abdominal PPI. This study proposed respiratory motion-compensation (rMoCo) employing non-negative matrix factorization combined with fast block matching algorithm to effectively remove these disturbances on abdominal PPI, which was validated through in-vivo perfusion experiments. The mean calculation efficiency of rMoCo was improved by 83.6% when the algorithm was accelerated in a unique matching sequence, which was formed from dozens of DCEUS subsequences at the same respiratory phase. The horizontal and vertical displacements induced by respiratory kinetics were estimated to correct the extraction of time-intensity curves and the peak SNR remained at 22.58±2.90dB. Finally, the abdominal PPIs of four perfusion parameters were formed with non-negative rMoCo, and their SCR was improved by 3.99±0.49dB (p

In this paper, we propose a new video Super-Resolution (SR) method by jointly modeling intra-frame redundancy and inter-frame motion context in a unified deep network. Different from conventional methods, the proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) investigates both spatial and temporal residues, which are represented by the difference between a high resolution (HR) frame and its corresponding low resolution (LR) frame and the difference between adjacent HR frames, respectively. This spatial-temporal residual learning model is then utilized to connect the intra-frame and inter-frame redundancies within video sequences in a recurrent convolutional network and to predict HR temporal residues in the penultimate layer as guidance to benefit estimating the spatial residue for video SR. Extensive experiments have demonstrated that the proposed STR-ResNet is able to efficiently reconstruct videos with diversified contents and complex motions, which outperforms the existing video SR approaches and offers new state-of-the-art performances on benchmark datasets.

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.

Transformed domain sparsity of Magnetic Resonance Imaging (MRI) has recently been used to reduce the acquisition time in conjunction with compressed sensing (CS) theory. Respiratory motion during MR scan results in strong blurring and ghosting artifacts in recovered MR images. To improve the quality of the recovered images, motion needs to be estimated and corrected. In this article, a two-step approach is proposed for the recovery of cardiac MR images in the presence of free breathing motion. In the first step, compressively sampled MR images are recovered by solving an optimization problem using gradient descent algorithm. The L1-norm based regularizer, used in optimization problem, is approximated by a hyperbolic tangent function. In the second step, a block matching algorithm, known as Adaptive Rood Pattern Search (ARPS), is exploited to estimate and correct respiratory motion among the recovered images. The framework is tested for free breathing simulated and in vivo 2D cardiac cine MRI data. Simulation results show improved structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE) with different acceleration factors for the proposed method. Experimental results also provide a comparison between k-t FOCUSS with MEMC and the proposed method.

Motion estimation has traditionally been used in video encoding only, however, it can also be used to solve various real-life problems. Nowadays, researchers from different fields are turning towards motion estimation. Motion estimation has become a serious problem in many video applications. It is a very important part of video compression technique and it provides improved bit rate reduction and coding efficiency. The process of motion estimation is used to improve compression quality and it also reduces computation time. Block-based motion estimation algorithms are used as they require less memory for processing of any video file. It also reduces the complexity involved in computations. In this article, various block-matching motion estimation algorithms are discussed such as Full search (FS) or Exhaust Search, Three-Step search (TSS), New Three-Step search (NTSS), Four-Step search (FSS), Diamond search (DS) etc.

The current work presents a novel hybrid approach for motion estimation of various video sequences with a purpose to speed up the entire process without affecting the accuracy. The method integrates the dynamic Zero motion pre-judgment (ZMP) technique with Initial search centers (ISC) along with half way search termination and Small diamond search pattern. Calculation of the initial search centers has been shifted after the process of zero motion pre-judgment unlike most the previous approaches so that the search centers for stationary blocks need not be identified. Proper identification of ISC dismisses the need to use any fast block matching algorithm (BMA) to find the motion vectors (MV), rather a fixed search pattern such as small diamond search pattern is sufficient to use. Half way search termination has also been incorporated into the algorithm which helps in deciding whether the predicted ISC is the actual MV or not which further reduced the number of computations. Simulation results of the complete hybrid approach have been compared to other standard methods in the field. The method presented in the manuscript ensures better video quality with fewer computations.

The detection of frame deletion forgery is of great significance in the field of video forensics. Existing approaches, however, are not applicable to video sequences with variable motion strengths. In addition, the impact of interfering frames has not been considered in these approaches. Our research aims to develop a motion-adaptive forensic method as well as to eliminate interfering frames. Through a study of the statistical characteristics of the most common interfering frames such as relocated I-frames, we develop a new fluctuation feature based on frame motion residuals to identify frame deletion points (FDPs). The fluctuation feature is further enhanced by an intra-prediction elimination procedure so that it can be adapted to sequences with various motion levels. The enhanced feature is measured using a moving window detector to identify the location of a FDP. Finally, a postprocessing procedure is proposed to eliminate the minor interferences of sudden lighting change, focus vibration, and frame jitter. Our experimental results demonstrate that for videos with variable motion strengths and different interfering frames, the true positive rate of the algorithm can reach 90% when the false alarm rate is 0.3%. Our proposed method could provide a foundation for many practical applications of video forensics.

Video quality assessment (VQA) aims to predict the perceptual quality for improving the performance of practical application systems. However, the traditional methods consider the video as a sequence of two-dimensional images, which conflicts with the fact that a video signal is a three-dimensional volume data. This operation ignores the temporal information and results in a poor consistency with human perception. Hence, the paper presents a novel VQA model by exploring and exploiting the compact representation of energy in the three-dimensional discrete cosine transform (3D-DCT) domain. First, the video is transformed by 3D-DCT for every group of frame (GOF). Then three types of statistical features are derived from the 3D-DCT coefficients to represent energy compaction properties for simulating the process of human visual system (HVS). The parameters of the generalized Gaussian distribution (GGD) are estimated to imitate the marginal distribution of the 3D-DCT coefficients. Three energy ratios are calculated to depict how the video energy distributes over different frequency components. And the mean and variance value of absolute 3D-DCT coefficients are employed to measure the frequency variation of the video. Finally, the differences between the feature of reference video and the feature of distorted video are calculated to predict the quality score of the distorted video. Experimental results show that the proposed VQA method has a good consistency with human perception and is competitive with the state-of-the-art methods.