Block Based Motion Vector Estimation Using FUHS16, UHDS16 and UHDS8 Algorithms for Video Sequence

Block-matching algorithm is the most common technique applied in block-based motion estimation technique. There are several block-matching algorithm based on block-based motion estimation techniques have been developed. Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. These techniques are applied to video sequences to remove the temporal redundancy for compression purposes and to gauge the motion vector estimation. In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. In order to develop the proposed methods, the baseline techniques are studied to develop the proposed algorithms. This chapter proposes modelling of fast unrestricted hexagon search (FUHS16) and unrestricted hexagon-diamond search (UHDS16) algorithms for motion vector estimation, which is based on the theory and application of block-based motion estimation. Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. These parameters have been used to measure the experimental results. It is the aim of this study that this work provides a common framework with which to evaluate and understand block-based matching motion estimation performance. On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. Various existing block-matching motion estimation algorithms have been analysed to develop the fundamental research. Based on the theoretical and fundamental research analysis the FUHS16 and UHDS16 algorithms using 16 × 16 block-based motion estimation formulations were developed. To improve the UHDS16 algorithm, 8 × 8 block-matching technique has been tested. The 8 × 8 block-matching technique is known as UHDS8. The results show positive improvements. From an application perspective, the UHDS8 algorithm efficiently captured the motion vectors in many video sequences. For example, in video compression, the use of motion vectors on individual macro-blocks optimized the motion vector information. The UHDS8 algorithm also offers improvement in terms of image quality performance, computational complexity and elapsed processing time. Thus, this chapter offers contributions in certain areas such as reducing the mechanism of computational complexity in estimating the motion from the video sequences. In particular, the FUHS16, UHDS16 and UHDS8 algorithms were developed to estimate the motion vectors field in the video sequences. Theoretical analysis block-based matching criteria are adapted to FUHS16, UHDS16 and UHDS8 algorithms, which are based on search points technique. Basically, the proposed of FUHS16, UHDS16 and UHDS8 algorithm produces the best motion vector estimation finding based on the block-based matching criteria. Besides that, the UHDS8 algorithm also improves the image quality performances and the search points in terms of the computational complexity. Overall, the study shows that the UHDS8 algorithm produces better results compared to the FUHS16 and UHDS16 algorithm

A Low-complexity Wavelet Based Algorithm for Inter-frame Image Prediction

In this paper, a novel multi-resolution variable block size algorithm (MRVBS) is introduced. It is based on: (1) Using the wavelet components of the seven sub-bands from two layers of wavelet pyramid in the lowest resolution; (2) Performing a block matching estimation within a nine-block only in each sub-band of the lower layer; (3) Scaling the estimated motion vectors and using them as a new search center for the finest resolution. The motivation for using the multi-resolution approach is the inherent structure of the wavelet representation. A multi-resolution scheme significantly reduces the searching time, and provides a smooth motion vector field. The approach presented in this paper providing an accurate motion estimate even in the presence of single and mixed noise. As a part of this framework, a comparison of the Full search (FS) algorithm, the three-step search (TSS) algorithm and the new algorithm (MRVBS) is presented. For a small addition in computational complexity over a simple TSS algorithm, the new algorithm achieves good results in the presence of noise

A Novel Search Technique of Motion Estimation for Video Compression

Video Compression is highly demanded now a days as due to the fact that in the field of entertainment, medicine and communication there is high demand for digital video technology. For the effective removal of temporal redundancy between the frames for better video compression Motion estimation techniques plays a major role. Block based motion estimation has been widely used for video coding. One such method is the Hierarchical Search Technique for BMA. By amalgamating the three different search algorithms like New three step search, New Full search and New Cross diamond search a novel hierarchical search methodology is proposed. Sub- sampling the original image into additional two levels is done and thereby the New Diamond search algorithm and a new three-step search algorithm are used in the bottom two levels and the Full Search is performed on the highest level where the complexity is relatively low. In terms of PSNR with reduced complexity this new proposed algorithm showed better performance

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

New motion estimation techniques and their SIMD implementations for video coding

Compression of video signals is of great importance to modern multi-media systems. In order to achieve efficient data compression, block motion estimation is generally employed to remove temporal redundancies inherent in video signals and thus, it is a crucial component of international video coding standards. This thesis aims at developing techniques to reduce the computational complexity of a given block motion estimation algorithm without sacrificing its accuracy, to utilize the single instruction multiple data (SIMD) technique to accelerate a block motion estimation process, and to develop a new fast block motion estimation algorithm suitable for implementation using SIMD architecture. A method to detect blocks that are stationary between successive frames, is proposed. In this method, when a block is judged as stationary, the search process for such a block is skipped in the block motion estimation process. The statistical characteristics of the video sequence are utilized in deciding as to which blocks are stationary. Simulation studies are carried out showing that this method reduces the computational complexity of the various block motion estimation algorithms without sacrificing the accuracy of the original algorithm. A vector-based fast block motion estimation algorithm, suitable for implementation on an SIMD architecture, is proposed. This algorithm maintains the accuracy and coding efficiency of the full-search algorithm, but the complexity is only a very small fraction of that of the full-search algorithm. It is also shown that by implementing the proposed algorithm on an SIMD architecture, the execution time of the algorithm can be further reduced by about 74%. The concept of an eight-bit partial sum is introduced so as to take advantage of the byte-type data parallelism in the existing SIMD architectures. A method of employing these partial sums to speedup a given block motion estimation process is proposed. The notion of the eight-bit partial sums is extended to the four-level case and it is shown that there are fifteen possible methods of utilizing these multi-level partial sums to accelerate block motion estimation algorithms. It is shown that any of these fifteen methods can accelerate a given block motion estimation algorithm without any loss of accuracy. The full-search algorithm is used to determine as to which one of these fifteen methods would provide the lowest computational complexity in order for it to be chosen to accelerate the various motion estimation algorithms. Simulation studies have been conducted and the results show that the proposed scheme is capable of providing a substantial speedup for the various existing motion estimation algorithms without any loss of accuracy

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

Block matching algorithm based on Harmony Search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate.Comment: 25 Pages. arXiv admin note: substantial text overlap with arXiv:1405.472