Performance analysis of Orthogonal - Diamond Search algorithm for Motion Estimation

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.

Many fast Block Matching Algorithm or BMA have been proposed and developed in recent years in order to reduce the computational cost while maintaining the quality of the video signal. In this paper, a new Orthogonal-Diamond Search (ODS) is proposed which employs an orthogonal-shaped search pattern in the first step and then is switched into small diamond-shaped search pattern for the next step. The proposed algorithm and several established algorithms, namely Full Search (FS), Three-Step Search (TSS) and Diamond Search (DS) are implemented using MATLAB and their performance are being compared and analyzed in terms of computational complexity, peak signal-to-noise ratio (PSNR), and number of search points. Simulation result shows that the proposed algorithm can find motion vector with fewer number of search points while maintains close performance of video quality with other selected algorithms.

To enable wireless Internet and other data services using mobile appliances, there is a critical need to support content rich cellular data communication, including voice, text, image and video. However, mobile communication of multimedia content has several bottlenecks, including limited bandwidth of cellular networks, channel noise, and battery constraints of the appliances. This paper is a review of the block matching algorithms used for motion estimation in video compression. It implements and compares 7 different types of block matching algorithms such as, Exhaustive Search, Three Step Search (TSS), New Three Step Search (NTSS), Simple and Efficient Search (SES), Four Step Search (4SS), Diamond Search (DS), Adaptive Rood Pattern Search (ARPS) and conclude the better block estimation algorithm for video compression. The algorithms that are evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards. Of the various algorithms studied and simulated ARPS turns out to be the better block matching algorithm for motion estimation.

In single viewpoint video coding, there are many fast block matching motion estimation algorithms proposed, such as three-step search, four-step search, diamond search as well as hexagon-based search and so on. However, experimental analysis show that these algorithms are not suitable for using directly in Multiview View Coding (MVC). Since the increased search range, the larger format of multiview video as well as the correlations between inter-view frames are not considered by these algorithms, they may easy led the block matching search into local minimum, the Rate-Distortion (R-D) performance will degrade dramatically. In this paper, we propose a novel multiple hexagon search algorithm to address this problem. Firstly, according to the original initial search point, four sub-search windows are constructed. Then, the hexagon based search algorithm will be performed respectively in the four sub-search windows. The final result is the best search point with the minimum R-D cost among the best points in the four sub-search windows. In order to trade off the computational complexity and R-D performance, two adaptive early termination strategies are proposed. The experimental results show that the proposed algorithm yields a quite promising coding performance in terms of R-D performance and computational complexity. Especially, the proposed algorithm can work well in multiview video sequences with various motion and disparity activities.

The full search block-matching algorithm is the simplest, but computationally very intensive approach. In recent years there was substantial progress in block motion estimation algorithms. Important milestones on this path were such algorithms as two-dimensional logarithmic search, three-step search, four step search, and diamond search. All these methods try to minimize the amount of search points applying sum of absolute differences (SAD) or equivalent metrics for each point. There were some works that studied partial SAD (PSAD) but mostly concentrated on a constant decimation factor. What we tried to study in this work is the performance of one of the best block matching search algorithms in combination with adaptive PSAD as a matching metric. The idea is that we use original motion estimation based on spatio-temporal correlation method, but instead of using SAD as a matching metric we use PSAD with adaptively chosen decimation factor. Our simulation results show that for high motion sequences PSNR degradation between full search and the proposed method was around 0.1-0.7 dB. The computational complexity reduction of 650-1700 times (compared with the full search) and 9 times (compared to the original method) is pretty big and may be well worth this video quality decrease. In the case of more static sequences PSNR degradation between full search and the original motion estimation method was around 0 dB. When we compare the original method with the proposed one the degradation increases to 0.1 dB. The computational complexity reduction was around 1600-1700 times (compared with the full search) and 5-7 times (compared to the original method).

In this paper, we design a flexible VLSI-based parallel processing system for an improved three-step search (ITSS) motion estimation algorithm that is superior to the existing three-step search (TSS) algorithm in all cases and also to the recently proposed new three-step search (NTSS) algorithm if used for low bit-rate video coding, as with the H.261 standard. Based on a VLSI tree processor and an FPGA addressing circuit, the proposed architecture can successfully implement the ITSS algorithm on silicon with the minimum number of gates. Because of the flexibility of the architecture, it can also be extended to implement other three-step search algorithms.

In block motion estimation, a search pattern with a different shape or size has a very important impact on search speed and distortion performance. A square-shaped search pattern is adopted in many popular fast algorithms. Recently, a diamond-shaped search pattern was introduced in fast block motion estimation and has exhibited a faster search speed. Based on an in-depth examination of the influence of the search pattern on speed performance, we propose a novel algorithm using a hexagon-based search pattern to achieve further improvement. The hexagon-based search pattern is investigated in comparison with diamond search pattern and demonstrates significant speedup gain over the diamond-based search. Analysis shows that a speed improvement rate of the hexagon-based search (HEXBS) algorithm over the diamond search (DS) algorithm can be over 80% for locating some motion vectors in certain scenarios. In short, the proposed HEXBS algorithm can find the same motion vector with fewer search points than the DS algorithm. Generally speaking, the larger the motion vector, the more search points the. HEXBS algorithm can save, which is further justified by experimental results.

Many suboptimal motion vector search algorithms have been proposed because the full search algorithm, which is an optimal method, requires huge computational requirements. These algorithms find motion vectors simply from the center of the search window. In this paper we propose an efficient motion vector search algorithm (GLS), which exploits the global motion information obtained from the previous three frames and the local motion information regarding the motion vectors of the neighboring blocks of the current block in order to predict the initial search point. GLS searches for a motion vector from this initial search point, instead of the center of the search window, using either the diamond search algorithm (DS) or the unrestricted small diamond search algorithm (USDS) which performs its search always with a smaller diamond search pattern. USDS has the same search pattern as that of the last search step in DS. Experimental results show that GLS is a faster and more accurate motion vector search method than other suboptimal methods.

Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10–15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to ∼316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.

In this paper, a new fast block matching algorithm is developed for motion estimation. The motion estimation algorithm use improved 3-step block matching (ITSS) algorithm that can produces better quality performance and less computational time compared with three-step search (TSS) algorithm and correct the motion vector by using Zero Detection and vector filter. From the experimental results, the proposed algorithm is superior to TSS in both quality performance and computational complexity.

In this paper we propose a block matching algorithm (BM) that reduces the computational burden of the motion estimation in an hybrid video encoder. To decrease the global complexity it acts at three different levels represented by the frame itself, the searching window and the macroblock (MB). To achieve an optimal performance also onto current general purpose processors provided with SIMD register files, a particular sub-sampling pattern of the MB is used. The algorithm shows a lower complexity than the three step search (TSS). In sequences where the motion field is formed by large motion vectors, the quality performance can be better than the full search algorithm (FSA) with an a priori fixed maximum motion vector. The algorithm is in fact able to adapt itself to the sequence.

Coding of Video Sequences Using Three Step Search Algorithm

In this paper, a modified diamond search (MDS) algorithm is proposed for fast motion estimation based on the well known diamond search (DS) algorithm. A set of computationally efficient algorithms that can be applied to any block matching algorithm and is applied to the DS as a study case achieves higher complexity reduction than DS algorithm without further relative PSNR (peak signal to noise ratio) degradation compared to full search (FS). First, dynamic internal stop search (DISS) algorithm is used to reduce the internal redundant SAD (sum of absolute difference) operations between the current and the candidate blocks using an accurate dynamic threshold. Second, a dynamic external stop search (DESS) greatly reduces the unnecessary operations by skipping all the irrelevant blocks in the search area. In addition, early search termination and adaptive pattern selections techniques are applied to the proposed MDS as initialization steps to achieve even higher complexity reduction. The accuracy of the proposed model threshold equations guarantee not to fall into a local minima. Experiments show that the proposed MDS algorithm reduces the computations greatly up to 99% and 20% compared with the conventional FS algorithm and DS respectively with no significant degradation in both the PSNR and the bit-rate.

This paper proposes two enhanced cross-diamond-hexagonal search algorithms to solve the motion-estimation problem in video coding. These algorithms differ from each other by their second step search only, and both of them employ cross-shaped pattern in first step. Proposed method is an improvement over CDHS, which eliminates some checking points of CDHS algorithm. Experimental results show that the proposed methods perform faster than the diamond search (DS) and CDHS, whereas similar quality is preserved.

The three step search (TSS) has been used for real time video processing and low bit-rate video communications because of reduced computations, simplicity, and reasonable performance in the motion estimation. Other modified algorithms of TSS have been studied for speed of computation and the performance of motion vector estimation. This paper proposes a new algorithm (Fast Three Step Search) for more efficient reduction of computational complexity. It is shown that the proposed algorithm (FTSS) is very efficient in computation while keeping the reasonable performance.