An improved block matching algorithm for motion estimation invideo sequences and application in robotics

Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms

An improved block matching algorithm for motion estimation in video sequences and application in robotics

Block Matching is one of the most efficient techniques for motion estimation for video sequences. Metaheuristic algorithms have been used effectively for motion estimation. In this paper, we propose two hybrid algorithms: Artificial Bee Colony with Differential Evolution and Harmony Search with Differential Evolution based motion estimation algorithms. Extensive experiments are conducted using four standard video sequences. The video sequences utilized for experimentation have all essential features such as different formats, resolutions and number of frames which are generally required in input video sequences. We compare the performance of the proposed algorithms with other algorithms considering various parameters such as Structural Similarity, Peak Signal to Noise Ratio, Average Number of Search Points etc. The comparative results demonstrate that the proposed algorithms outperformed other algorithms

Hybrid Approach for Video Compression Using Block Matching Motion Estimation

To discard the redundancy present in video some video compression technique are involved .Basically video is a collection sequential frames in a sequence. video compression means reducing the size of video . In video sequence there are two types of technique are present that are temporal redundancy and spatial redundancy. In this paper we discuss about hybrid technique .Hybrid means combination of any two or more than two technique like efficient three step search algorithm(E3SS) and cross hexagonal search algorithm (CHS) .In today’s date block matching algorithm for motion estimation is powerful technique for high compression ratio and to reduce computational complexity .The motion estimation calculate the position of pixel and It is a custom to calculate the pixel from current frame to reference frame .The main function of motion estimation is reducing the search point and redundancy present in video .The experiment result shows that the proposal algorithm performs better than previous proposed block matching algorithms and required less computation than other technique

New techniques for the design and implementation of efficient full-search algorithms for block-matching motion estimation

The block-matching motion estimation (BME) is one of the most commonly used techniques for digital video compression in low to moderate bit rate environments. The full search for block-matching motion estimation, as compared to a partial search, provides a higher motion estimation accuracy, yet its computational cost is generally high. Hence, developing new techniques for an efficient implementation of full-search algorithms is of practical significance for the BME. In this thesis, a new full search algorithm is proposed, wherein the mean squared error (MSE) is used as the matching criterion to provide a higher motion estimation accuracy for the BME than that by any algorithm based on the most commonly-used mean absolute difference. It is shown that the computation of the MSE in the Haar wavelet domain results in a computational complexity that is much lower than or of the same order as that of the best-performing full search algorithms available in the literature. A new approach has been developed for the multi-reference-frame block-matching motion estimation, wherein a full search is performed in the spatial domain of the multi-reference-frame memory, and an early termination is imposed in the temporal domain using a novel strategy. It is shown that the computational complexity of the proposed full search method is significantly lower than that of any existing full search technique, and yet has a motion estimation accuracy which is about the same as that of the latter. A new pseudo-spiral-scan data input scheme has been proposed, which can be used in any existing hardware architecture for the implementation of the successive-elimination-based block-matching motion estimation. This scheme results in significant power savings compared to the conventional raster-scan data input scheme. Several designs to implement the successive elimination algorithm have been given, some of which are shown to provide additional power savings

Motion estimation and video coding

Over the last ten years. research on the analysis of visual motion has come to play a key role in the fields of data compression for visual communication as well as computer vision. Enormous efforts have been made on the design of various motion estimation algorithms. One of the fundamental tasks in motion estimation is the accurate measurement of 2-D dense motion fields. For this purpose. we devise and present in this dissertation a multiattribute feedback computational framework. In this framework for each pixel in an image. instead of a single image intensity. multiple image attributes are computed as conservation information. To enhance the estimation accuracy. feedback technique is applied. Besides. the proposed algorithm needs less differentiation and thus is more robust to various noises. With these features. the estimation accuracy is improved considerably. Experiments have demonstrated that the proposed algorithm outperforms most of the existing techniques that compute 2-D dense motion fields in terms of accuracy. The estimation of 2-D block motion vector fields has been dominant among techniques in exploiting the temporal redundancy in video coding owing to its straightforward implementation and reasonable performance. But block matching is still a computational burden in real time video compression. Hence. efficient block matching techniques remain in demand. Existing block matching methods including full search and multiresolution techniques treat every region in an image domain indiscriminately no matter whether the region contains complicated motion or not. Motivated from this observation. we have developed two thresholding techniques for block matching in video coding. in which regions experiencing relatively uniform motion are withheld from further processing via thresholfing. thus saving compu­tation drastically. One is a thresholding multiresolution block matching. Extensive experiments show that the proposed algorithm has a consistent performance for sequences with different motion complexities. It reduces the processing time ranging from 14% to 20% while maintaining almost the same quality of the reconstructed image (only about 0.1 dB loss in PSNR). compared with the fastest existing multiresolution technique. The other is a thresholding hierarchical block matching where no pyramid is actually formed. Experiments indicate that for sequences with less motion such as videoconferencing sequences. this algorithm works faster and has much less motion vectors than the thresholding multiresolution block matching method

Modified Three-Step Search Block Matching Motion Estimation and Weighted Finite Automata based Fractal Video Compression

The major challenge with fractal image/video coding technique is that, it requires more encoding time. Therefore, how to reduce the encoding time is the research component remains in the fractal coding. Block matching motion estimation algorithms are used, to reduce the computations performed in the process of encoding. The objective of the proposed work is to develop an approach for video coding using modified three step search (MTSS) block matching algorithm and weighted finite automata (WFA) coding with a specific focus on reducing the encoding time. The MTSS block matching algorithm are used for computing motion vectors between the two frames i.e. displacement of pixels and WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image (frame or motion compensated prediction error) based on the idea of fractal that the image has self-similarity in itself. The self-similarity is sought from the symmetry of an image, so the encoding algorithm divides an image into multi-levels of quad-tree segmentations and creates an automaton from the sub-images. The proposed MTSS block matching algorithm is based on the combination of rectangular and hexagonal search pattern and compared with the existing New Three-Step Search (NTSS), Three-Step Search (TSS), and Efficient Three-Step Search (ETSS) block matching estimation algorithm. The performance of the proposed MTSS block matching algorithm is evaluated on the basis of performance evaluation parameters i.e. mean absolute difference (MAD) and average search points required per frame. Mean of absolute difference (MAD) distortion function is used as the block distortion measure (BDM). Finally, developed approaches namely, MTSS and WFA, MTSS and FC, and Plane FC (applied on every frame) are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, akiyo, bus, mobile, suzie, traffic, football, soccer, ice etc. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio and Peak Signal to Noise Ratio (PSNR). The video compression using MTSS and WFA coding performs better than MTSS and fractal coding, and frame by frame fractal coding in terms of achieving reduced encoding time and better quality of video

Classification-Based Adaptive Search Algorithm for Video Motion Estimation

A video sequence consists of a series of frames. In order to compress the video for efficient storage and transmission, the temporal redundancy among adjacent frames must be exploited. A frame is selected as reference frame and subsequent frames are predicted from the reference frame using a technique known as motion estimation. Real videos contain a mixture of motions with slow and fast contents. Among block matching motion estimation algorithms, the full search algorithm is known for its superiority in the performance over other matching techniques. However, this method is computationally very extensive. Several fast block matching algorithms (FBMAs) have been proposed in the literature with the aim to reduce computational costs while maintaining desired quality performance, but all these methods are considered to be sub-optimal. No fixed fast block matching algorithm can effi- ciently remove temporal redundancy of video sequences with wide motion contents. Adaptive fast block matching algorithm, called classification based adaptive search (CBAS) has been proposed. A Bayes classifier is applied to classify the motions into slow and fast categories. Accordingly, appropriate search strategy is applied for each class. The algorithm switches between different search patterns according to the content of motions within video frames. The proposed technique outperforms conventional stand-alone fast block matching methods in terms of both peak signal to noise ratio (PSNR) and computational complexity. In addition, a new hierarchical method for detecting and classifying shot boundaries in video sequences is proposed which is based on information theoretic classification (ITC). ITC relies on likelihood of class label transmission of a data point to the data points in its vicinity. ITC focuses on maximizing the global transmission of true class labels and classify the frames into classes of cuts and non-cuts. Applying the same rule, the non-cut frames are also classified into two categories of arbitrary shot frames and gradual transition frames. CBAS is applied on the proposed shot detection method to handle camera or object motions. Experimental evidence demonstrates that our method can detect shot breaks with high accuracy

Motion estimation based frame rate conversion hardware designs

Frame Rate Up-Conversion (FRC) is the conversion of a lower frame rate video signal to a higher frame rate video signal. FRC algorithms using Motion Estimation (ME) obtain better quality results. Among the block matching ME algorithms, Full Search (FS) achieves the best performance since it searches all search locations in a given search range. However, its computational complexity, especially for the recently available High Definition (HD) video formats, is very high. Therefore, in this thesis, we proposed new ME algorithms for real-time processing of HD video and designed efficient hardware architectures for implementing these ME algorithms. These algorithms perform very close to FS by searching much fewer search locations than FS algorithm. We implemented the proposed hardware architectures in VHDL and mapped them to a Xilinx FPGA. ME for FRC requires finding the true motion among consecutive frames. In order to find the true motion, Vector Median Filter (VMF) is used to smooth the motion vector field obtained by block matching ME. However, VMFs are difficult to implement in real-time due to their high computational complexity. Therefore, in this thesis, we proposed several techniques to reduce the computational complexity of VMFs by using data reuse methodology and by exploiting the spatial correlations in the vector field. In addition, we designed an efficient VMF hardware including the computation reduction techniques exploiting the spatial correlations in the motion vector field. We implemented the proposed hardware architecture in Verilog and mapped it to a Xilinx FPGA. ME based FRC requires interpolation of frames using the motion vectors found by ME. Frame interpolation algorithms also have high computational complexity. Therefore, in this thesis, we proposed a low cost hardware architecture for real-time implementation of frame interpolation algorithms. The proposed hardware architecture is reconfigurable and it allows adaptive selection of frame interpolation algorithms for each Macroblock. We implemented the proposed hardware architecture in VHDL and mapped it to a low cost Xilinx FPGA

Algorithms for motion estimation.

This thesis presents several block matching techniques which are utilized for motion estimation in video coding. Among these algorithms the full search algorithm is known for its superiority in the performance over the other matching techniques; this method however requires an enormous computation costs. There are reported techniques in the literature which yield sub-optimal solutions. These are discussed in this thesis while another three efficient motion estimation algorithms for different applications are developed and presented in this thesis. The main objective of these proposed algorithms is to reduce the computation cost while maintaining the performance. Experimental results with a large data set prove the utility of proposed method. Also this thesis presents an architecture for the implementation of one of the proposed ME algorithms. This architecture uses a scheme named dynamic voltage scaling (DVS) capable of changing the power supply according the workload demand of the circuit and enjoys a low power characteristic in CMOS technology. This thesis also presents a discussion on the implementation of DVS in motion estimation circuits. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .H83. Source: Masters Abstracts International, Volume: 42-02, page: 0646. Adviser: M. Ahmadi. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003