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

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

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

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

Hierarchical motion estimation based on visual patterns for video coding

Block matching algorithms(BMAs) are often employed for motion estimation(ME) in video coding. Most conventional fast BMAs treat the ME problem as an optimization problem and suffer heavily from the problem of being trapped at local minima. The full search algorithm(FS), on the other hand, is very time-consuming. Few of them makes use of the information inherent in the images explicitly. We propose a new ME algorithm which can reduce the search range while guaranteeing global optimality in most cases, making use of the edge features. Microblock visual patterns are designed to extract edge information to guide block matching: searching is only carried out at places where the real match most likely happens. The motion field subsampling technique is further employed to get a hierarchical algorithm, which can further double the speed. The proposed algorithms obtain speeds about ten times faster than that of FS with comparable prediction quality.published_or_final_versio

Adaptive Diamond Search Algorithm for Motion Estimation

Implementation of the Block Matching Algorithm (BMA) in Motion Estimation (ME) has been widely used in video encoder due to its simplicity and high compression efficiency. Many fast search methods of BMAs are being introduced to increase the efficiency  of the ME  process. This paper proposed a new algorithm, namely Adaptive Diamond Search Algorithm (ADS) which employs three different search patterns for its two main stages. At the initial step, an additional step is added to a predetermined static block to further speed up the search process as it is beneficial to small motion video sequence contents. The performances of the ADS are then compared with three selected established algorithms, namely the Full Search (FS), Diamond Search (DS) and Hexagon-Diamond Search (HDS). Based on the simulation result, the proposed algorithm yields a very good video quality performance with fewer search points compared with other algorithms

Fast Motion Estimation Algorithm using Hybrid Search Patterns for Video Streaming Application

The objective of the paper is to develop new block matching Motion Estimation (ME) algorithm using hybrid search patterns along the direction of best match. The search efficiency for sequences with fast motions and high resolutions is improved by proposing New Cross Diagonal-Hexagon Search (NCDHS) algorithm which involves a novel multi half-hexagon grid global search pattern and a cross diagonal-hexagon local search pattern. The new search pattern enables the proposed algorithm to perform better search using 9.068 search points on an average, to obtain optimal motion vector with slight improvement in quality. This inturn reduces ME Time upto 50.11%, 47.12%, 32.99% and 43.28% on average when compared to the existing Diamond Search (DS), Hexagon Search (HS), New Cross Hexagon Search (NHEXS) and Enhanced Diamond Search (EDS) algorithms respectively. The novelty of the algorithm is further achieved by applying the algorithm proposed for live streaming application. The NCDHS algorithm is run on two MATLAB sessions on the same computer by establishing the connection using Transmission Control Protocol (TCP) /Internet Protocol (IP) network. The ME Time obtained is 14.5986 seconds for a block size 16x16, is less when compared to existing algorithms and that makes the NCDHS algorithm suitable for real time streaming application

Performance Analysis of Hexagon-Diamond Search Algorithm for Motion Estimation using MATLAB

To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an algorithm called Hexagon-Diamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR), number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% while at the same time maintains close performance in terms of PSNR to others