Efficient Block Matching Algorithm Research Articles

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Modified Cuckoo Search Algorithm for Motion Vector Estimation

Motion estimation and motion compensation are the accepted process in H.264 and H.265 video coding standard to reduce temporal redundancy. Several fast block matching algorithms have been developed to reduce the calculation cost in the motion estimation process. But quick block matching algorithms often lead to a local minimum. Several researchers used different population-based nature-inspired algorithms to perform block matching. Algorithms like genetic algorithm, differential evolution, particle swarm optimization were used in numerous motion estimation algorithms. Different algorithms used a fitness approximation strategy to reduce computation cost. Jaya algorithm-based block matching is the most efficient block matching algorithm in the available literature. Jaya algorithm is free from algorithmic specific parameter which speeds up the process. This article proposes a few modifications to the traditional cuckoo search algorithm and then, a block matching algorithm was proposed based on the modified cuckoo search algorithm. Fitness approximation, adaptive termination, and zero motion prejudgment modules were used with the modified cuckoo search algorithm to reduce the number of redundant calculations. The performance of the proposed algorithm was compared with the exhaustive search algorithm and other benchmarking algorithms in terms of Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM), and average search point required to calculate a motion vector for a block. The proposed algorithm delivers better performance compared to the benchmarking algorithms.

Hardware efficient block matching algorithm based on modified differential evolution optimization for fast motion estimation

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.

Efficient block matching using improved particle swarm optimization with application to displacement measurement for nano motion systems

This paper is devoted to the micro vision based displacement measurement for a nano-positioning stage with the aid of a single camera. For this purpose, a pinhole imaging model as well as corresponding Jacobin matrix is established, with which an efficient block matching (BM) algorithm is developed based on the particle swarm optimization (PSO) algorithm. In particular, an improved PSO algorithm is developed by proposing a new fitness updating strategy (FUS) such that the particles’ migration can be performed with an efficient way in the whole optimization process. The improved PSO can significantly speed up the optimization process, but meanwhile resulting in a coarse-level estimation result. The full search algorithm (FSA) is thus performed on the coarse-level result in order to achieve the final fine-level result. The effectiveness of the presented method was demonstrated using the testing data acquired from an experimental platform, where comparative studies with those existing benchmark methods were also provided. The comparison results show that the method achieves a significant improvement on computational efficiency without loss of accuracy, which implies it has a better balance/trade-off between the measurement accuracy and computation efficiency in real applications.

Hardware Efficient Double Diamond Search Block Matching Algorithm for Fast Video Motion Estimation

Application of video in multimedia communication has become feasible due to efficient block matching algorithm (BMA) based motion estimation (ME) and motion compensation (MC) methods, that facilita...

A Fast Block-Matching Motion Estimation Method Based on Difference Search Algorithm

The diamond search (DS) algorithm is one of the most efficient block matching motion estimation algorithms by far and has already been applied in MPEG2/4. Through our research, we found that there is still some redundancy in the algorithm. In this paper, an improved new difference based search (DBS) algorithm is proposed. Simulation results demonstrate that the new algorithm outperforms the well-known diamond search (DS) algorithm and four step-searches (4SS). It obtains almost the same Peak Signal to Noise Ratio (PSNR) while requires less computations than the DS algorithm and 4SS algorithm.

An efficient block matching and spectral shift estimation algorithm with applications to ultrasound elastography

An efficient block matching and spectral shift estimation algorithm for freehand quasi-static ultrasound elastography is described in this paper. The proposed method provides a balance between computational speed and robustness against displacement estimation error and bias; a fundamental aspect of elastography. The new algorithm was tested on an extensive set of simulated 1-D RF ultrasound signals, replicating various strain profiles. Additionally, real 2-D scans were conducted on an ultrasound phantom with prescribed elastic properties; the algorithm output was further validated with a comparison to a finite element model (FEM) of the phantom. Clinical data from a breast cancer study and histology slides were used to demonstrate the in vivo use of the new elastography technique. The algorithm showed a significant computational savings (at least 60 times faster) over existing spectral shift analysis methods. Accurate strain images were produced in as little as 2 s with the scope for further speed enhancements through parallel processing; making real-time implementation a future possibility. Moreover, it demonstrated a robustness toward displacement estimation error when compared with conventional gradient-based techniques, and was able to perform at high strain values (>5%) while showing relative insensitivity to various parameters settings, such as sample rate and block window size; a desirable performance for a practical clinical tool.

Efficient block-matching motion estimation algorithm

Block-matching motion estimation (ME) is a principal process for most existing video compression standards, but it has a significant impact on computation overhead. An efficient block-matching algorithm is proposed for ME. The proposed algorithm consists of three effective steps: (1) apply the full search (FS) algorithm to construct a polynomial interpolation model from the group with four skipping frames to determine near-optimal global motion vector (MV) for the initial search point, (2) perform an adaptive search pattern in the interpolation step to refine the MV and (3) use the half-way stop technique to reduce search points. Experimental results show that the proposed algorithm achieves a maximum speed-up factor of 366.47 with only 0.52% peak signal-to-noise ratio (PSNR) degradation in comparison with the FS algorithm.

Histogram ordering model-based fast motion estimation

This study proposes two efficient block matching algorithms for fast motion estimation using a histogram ordering model in order to reduce the computational cost of video coding. Since the representative pixels that consist of edge or texture mainly contribute to the block matching criterion, it is important to analyse the details of the current block. To analyse the characteristics of the block with low complexity, the pixel histogram is used for the observation of the block. Based on this pixel histogram for the current block, an optimal block matching order is determined for the lossless fast matching algorithm. In addition, adaptive partial block matching algorithm for lossy fast motion estimation using histogram-based block matching ordering is also presented to further reduce the complexity of block matching. By capturing the representative pixels, block matching distortion is maximised soon enough during the block matching. For this reason, partial block matching is available instead of full block matching, which is also known as a full search algorithm. Our experimental results show that the proposed algorithm not only reduces the computational complexity of block matching criterion, but also tends to maintain the image quality when compared to the conventional fast matching algorithms.

Application of An Improved Motion Estimated Three Step Search Algorithm in infrared Video

Infrared video image compression is an important video processing technology infrastructure. This thesis proposes a efficient and fast block matching motion estimation algorithm. One strategy of this algorithm is to begin searching from two positions the predictive search centre and the(0,0)position, which earl still hold direction when the relatively predictive error is big. The other strategy is to adopt the Big Small Square search pattern, which chooses search step according to the moving state of objects. Experimental results show that the algorithm galls highly reduce computational complexity with little video quality dropping.

Interlaced scan CCD image motion deblur for space-variant motion blurs

Motion blur is caused by camera shakes or object motions during exposure when the shutter speed is relatively slow. As for the object motion blur, the degradation of a CCD image is often characterized by space-variant motion blurs, since objects are often moving in different directions at different speeds. But most image restorations for space-variant motion blurs are addressed only for progressive scan CCD images. To address the space-variant image restorations for interlaced scan images, we propose a novel image restoration scheme. First, one interlaced scan image frame is required, which is divided into the odd field and the even field images. These two field images are further segmented into rectangular blocks. The motion vectors are computed in these rectangular blocks using an efficient block matching algorithm. Second, image restoration is performed in these rectangular blocks using a constrained least square algorithm in the odd or even field image, which can both preserve edge structures and remove noises. Our novel scheme is illustrated by restoring a space-variant blurred moving boat image and a synthetic blurred image.

Image restoration for interlaced scan CCD image with space-variant motion blurs

When the speeds of objects in a scene exceed the temporal resolution of the camera shutter, motion blurs will occur. Since objects are often moving in different directions at different speeds, the degradation of a CCD image is often characterized by space-variant motion blurs. Image restoration algorithms for space-variant motion blurs are available for progressive scan CCD images, but not for interlaced scan images. To address the space-variant image restoration for interlaced scan images, a novel three-step image restoration scheme is proposed. Firstly, one interlaced scan image is divided into odd field and even field images. Secondly, these two field images are further segmented into rectangular blocks and the motion vectors are computed based on these rectangular blocks using an efficient block matching algorithm. Thirdly, image restoration is performed using a blind deconvolution algorithm in the odd or even field image. The final restored image is obtained by combining the restored odd and even field images. The scheme is illustrated by restoring a space-variant blurred moving vehicle image and a synthetic blurred image.

A fast block-matching algorithm based on variable shape search

Block-matching motion estimation plays an important role in video coding. The simple and efficient fast block-matching algorithm using Variable Shape Search (VSS) proposed in this paper is based on diamond search and hexagon search. The initial big diamond search is designed to fit the directional centre-biased characteristics of the real-world video sequence, and the directional hexagon search is designed to identify a small region where the best motion vector is expected to locate. Finally, the small diamond search is used to select the best motion vector in the located small region. Experimental results showed that the proposed VSS algorithm can significantly reduce the computational complexity, and provide competitive computational speedup with similar distortion performance as compared with the popular Diamond-based Search (DS) algorithm in the MPEG-4 Simple Profile.

Motion Estimation in Beating Heart Surgery

Minimally invasive beating-heart surgery offers substantial benefits for the patient, compared to conventional open surgery. Nevertheless, the motion of the heart poses increased requirements to the surgeon. To support the surgeon, algorithms for an advanced robotic surgery system are proposed, which offer motion compensation of the beating heart. This implies the measurement of heart motion, which can be achieved by tracking natural landmarks. In most cases, the investigated affine tracking scheme can be reduced to an efficient block matching algorithm allowing for realtime tracking of multiple landmarks. Fourier analysis of the motion parameters shows two dominant peaks, which correspond to the heart and respiration rates of the patient. The robustness in case of disturbance or occlusion can be improved by specially developed prediction schemes. Local prediction is well suited for the detection of single tracking outliers. A global prediction scheme takes several landmarks into account simultaneously and is able to bridge longer disturbances. As the heart motion is strongly correlated with the patient's electrocardiogram and respiration pressure signal, this information is included in a novel robust multisensor prediction scheme. Prediction results are compared to those of an artificial neural network and of a linear prediction approach, which shows the superior performance of the proposed algorithms.

Adaptive dual-cross search algorithm for block-matching motion estimation

In this paper, we present two new and efficient block-matching algorithm (BMAs) for fast motion estimation used in motion-compensated video compression. The proposed algorithms are improvement of existing BMAs and consist of three effective steps: 1) Initial search center prediction, 2) early search termination, and 3) dual-cross pattern search. Extensive simulation results and comparative analysis have shown that the proposed algorithms outperform conventional algorithms, like the three-step search, orthogonal search and diamond search, as well as the newly proposed algorithms, like the hexagonal search and adaptive rood pattern search, in terms of both video peak signal-to-noise ratio and the number of checking points are evaluated.

A statistical adaptive block-matching motion estimation

We address the problem of motion estimation (ME) in digital video sequences and propose a new fast, adaptive, and efficient block-matching algorithm. Higher quality and efficiency are achieved using a statistical model for the motion vectors. This model introduces adaptation in the search window, drastically reducing the number of positions where correlation-type computation is performed. The efficiency is further improved by progressively undersampling the macroblock. Patterns for undersampling are proposed to obtain the maximum benefit from single instruction multiple data (SIMD) instructions. In contrast with existing motion-estimation techniques, search strategy and subsampled patterns are closely linked. This shows that a good search strategy is much more important than blindly reducing the number of pixels considered for the matching pattern. We describe an implementation of the proposed matching strategy that exploits the very long instruction word (VLIW) and SIMD technology available in the new Itanium processor family. Results show that the proposed algorithm adapts easily to the evolution of the scene avoiding annoying quality drops that can be observed with other deterministic algorithms. The total number of operations required by the proposed method is inferior to those required by traditional approaches.

An efficient parallel motion estimation algorithm for digital image processing

The authors present an efficient block-matching algorithm called the parallel hierarchical one-dimensional search (PHODS) for motion estimation. Instead of finding the two-dimensional motion vector directly, the PHODS finds two one-dimensional displacements in parallel on the two axes (say x and y) independently within the search area. The major feature of this algorithm is that its search speed for the motion vector is faster than that of the other search algorithms on account of its simpler computations and parallelism. Compared with previous research in terms of four measurements, the PHODS can rival those algorithms for performance. The hardware-oriented features of the PHODS, i.e., regularity, simplicity, and parallelism, guarantee that the PHODS is more suitable for hardware realization of a VLSI motion estimator.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>