Block Matching Motion Estimation Research Articles

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

A search pattern based on the repeated motion vectors components for the fast block matching motion estimation in temporal coding

An efficient VLSI architecture for fast motion estimation by hybridizing parallel spiral and adaptive threshold star diamond search algorithm‐based search algorithm in HEVC

SummaryThis article proposes a high efficiency video coding (HEVC) standard hardware using block matching motion estimation algorithm. A hybrid parallel spiral and adaptive threshold star diamond search algorithm (Hyb PS‐ATSDSA) proposes for fast motion estimation in HEVC. Parallel spiral search approach utilizes spiral pattern for searching from center to the surroundings and adaptive threshold SDA consists of two phases, they are adaptive threshold and star diamond algorithm. To lower computational complexities in HEVC architecture, parallel spiral search algorithm uses several blocks matching schemes. Adaptive threshold and star diamond algorithm are used to reduce the matching errors and remove the invalid blocks early from the procedure of motion estimation and finally predicts the final motion of the image. Speed is increased while using this hybrid algorithm. Proposed structure is carried out in Xilinx; ISE 14.5 design suit, then the experimental outcomes are analyzed to existing motion estimation strategies in field‐programmable gate array devices. Experimental performance of the proposed Hyb‐PS‐ATSDSA‐ME‐HEVC method attains lower delay 33.97%, 32.97%, 62.97, and 26.97%, and lower area 34.867%, 45.97%, 27.97%, and 43.967% compared with the existing methods, such as FSA‐ME‐HEVC, TZSA‐ME‐HEVC, hyb TZSA‐IME‐HEVC, and IBMSA‐ME‐HEVC, respectively.

A cooperative three-player game theory approach for designing an ideal video steganography framework

ABSTRACT This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors are identified by Block Matching Motion Estimation Algorithm (BMMEA). Based on these motion vectors, the Region of Interest (ROI) is selected. The selected ROI is grouped using their momentum. In order to add an additional layer of security, the secret image is scrambled using pixel-wise Arnold Transform in the next step. Finally, based on the ROI groups, the scrambled secret data is embedded into the ROI's locations. An ideal and perfect video steganography system should be able to maintain the quality of the video after embedding the secret data. The quality factors of the video steganography approach include perceptible invisibility, payload capacity, and robustness. However, these quality factors are conflicted with each other. In order to address this issue, having these three quality factors as players a cooperative 3-players game theory approach is proposed. This technique provides an optimal solution for the video steganography framework using strategy adaption. The optimal solution is acquired by applying the Iterative Elimination of Strictly Dominant Strategies (IESDS) method. The achieved optimal solution comes up with the best trade-off between the quality factors. Experimental results have deduced the best optimal solution from the proposed three-player game theoretic approach that helps the video steganography approach to resolve the quality and security issues. It is also observed that the optimized proposed method outperforms the contemporary methods by attaining significant outcomes.

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL

Motion Estimation is one of the main tasks behind any video encoder. It is a computationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description languages is a complex task, it is desirable to use higher-level languages to develop FPGA applications.The aim of this work is to evaluate OpenCL, in terms of expressiveness, as a tool for developing this kind of FPGA applications. To do so, we present and evaluate a parallel implementation of the Block Matching Motion Estimation process using OpenCL for Intel FPGAs, usable and tested on an Intel Stratix 10 FPGA. The implementation efficiently processes Full HD frames completely inside the FPGA. In this work, we show the resource utilization when synthesizing the code on an Intel Stratix 10 FPGA, as well as a performance comparison with multiple CPU implementations with varying levels of optimization and vectorization capabilities. We also compare the proposed OpenCL implementation, in terms of resource utilization and performance, with estimations obtained from an equivalent VHDL implementation.

All Directional Search Motion Estimation Algorithm

The search performance of block-matching motion estimation algorithms, i.e., search speed and motion estimation quality, mainly depends on the shape and size of the search pattern. Most motion estimation algorithms employ search patterns of square, diamond, hexagonal, cross diamond or cross hexagonal shapes. These search patterns achieve good results for video sequences with simple motion activity, but unfavourable results for video sequences with complex motion activity. After a thorough investigation of the effect of search pattern on search performance, this paper proposes an all-direction search (ADS) pattern, which searches for the best block in all possible directions. For further improvement in search speed, a half way stop technique is applied in search process. The results show that the proposed ADS algorithm outperforms other state-of-the-art and eminent motion estimation algorithms. The higher the direction complexity in the neighbouring motion vectors, the better the prediction quality of ADS, which is further proved by the simulation results.

One-dimensional block-matching motion estimation algorithm

One-dimensional block-matching motion estimation algorithm

Maximum likelihood speed estimation of moving objects in video signals

Video processing solutions for motion analysis are key tasks in many computer vision applications, ranging from human activity recognition to object detection. In particular, speed estimation algorithms may be relevant in contexts such as street monitoring and environment surveillance. In most realistic scenarios, the projection of a framed object of interest onto the image plane is likely to be affected by dynamic changes mainly related to perspective transformations or periodic behaviours. Therefore, advanced speed estimation techniques need to rely on robust algorithms for object detection that are able to deal with potential geometrical modifications. The proposed method is composed of a sequence of pre-processing operations, that aim to reduce or neglect perspectival effects affecting the objects of interest, followed by the estimation phase based on the Maximum Likelihood (ML) principle, where the speed of the foreground objects is estimated. The ML estimation method represents, indeed, a consolidated statistical tool that may be exploited to obtain reliable results. The performance of the proposed algorithm is evaluated on a set of real video recordings and compared with a block-matching motion estimation algorithm. The obtained results indicate that the proposed method shows good and robust performance.

An efficient field-programmable gate array-based hardware oriented block motion estimation algorithm based on diamond adaptive rood pattern search algorithm for multi-standard video codec

Motion estimation and motion compensation techniques are effectively used for removing temporal redundancy between adjacent frames during video compression, but video quality will be degraded with the traditional pixel-based matching algorithms. To overcome this issue, diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithms are proposed in this manuscript for multi-stranded codec hardware design, which provide a high compression rate with less computational complexity. In this work, a diamond adaptive rood pattern search algorithm is used, which is a combination of a diamond-search algorithm and adaptive rood pattern search. In addition, the proposed algorithm applies multiple search strategies and terminates unnecessary searching while reserving high data regularity. The proposed diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithm for multi-stranded codec hardware design provides good performance in terms of complex calculations reduction and reaches a high speed in comparison to traditional search algorithms. The proposed architecture is implemented in Xilinx ISE design 14.5 suite and the implemented results are compared to the existing motion estimation techniques on field-programmable gate array devices. Simulation results shows that the diamond adaptive rood pattern search algorithm for multi-stranded codec hardware design provides a very good improvement in terms of computations overhead reduction, with 96.4%, 97.3% latency and 98.7% throughput and achieves a high compression rate without video quality degradation.

Motion Estimation for Gray Level Videos Using Different Block Matching Algorithms

Motion Estimation (ME) is a very important operation in video coding. In order to reduce complexity of computations involved in ME and to increase quality of this process, many Block Matching Motion Estimation (BMME) Algorithms are proposed. The aim of this paper is to compare between these algorithms and find the best one. Seven BMME algorithms are used in this paper. The performance of each algorithm is evaluated for different types of motion to determine the best one of these algorithms. The evaluation is based on search points, and Peak Signal to Noise Ratio (PSNR). The simulation shows that Hexagonal Search is faster than all other Block Matching (BM) algorithms used in this paper regardless the type of video because it requires less number of search points to evaluate motion vectors for the video sequence. It requires 11.2424 average search point (SP) for small motions and 13.9708 for fast motions. It also gives a good quality that is close enough to the quality given by Full Search

One-dimensional Block Matching Algorithm in Video Image Motion Estimation

<p indent=0mm>Motion estimation is a basic problem in the fields of video image compression and video image restoration. The traditional block matching methods have good search quality, but the search speed is not fast enough. Aiming at the shortcomings of the search speed in the traditional block matching methods, we proposed a fast one-dimensional block matching motion estimation algorithm. Firstly, the motion vector is orthogonally decomposed, and the two-dimensional matching block is reduced by a special weight coefficient matrix to obtain two sets of one-dimensional feature matrices. Then the one-dimensional three step search method is selected as the search strategy. The sum of absolute differences criterion is used as the matching criterion. The two sets of one-dimensional feature matrices are used to search for the two components of the matching motion vector. Finally the two components are formed into a complete motion vector. The results of multiple sets of comparative experiments show that the search speed of motion estimation is significantly improved while the algorithm is guaranteed to quantitatively evaluate the PSNR. The higher the video definition and the larger the pixel size of the matching block, the better the algorithm can improve the search speed of motion estimation.

Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.

A New Pentagon Search Algorithm for Fast Block-Matching Motion Estimation

The impact of search pattern is a crucial part in the block-based motion estimation for finding the motion vector. An issue of distortion performance and search speed heavily depends upon the size and shape of search strategy applied. Performing the deep analysis for motion vector distribution on standard test videos, it is desirable to have such type of algorithm that meets the requirement of searching motion vector in less time. Hence, a new kind of Pentagon algorithm is proposed in this paper for fast block-matching motion estimation (BMME). It is an easy and efficient technique for finding motion vector. Experimental results expose the proposed Pentagon algorithm sparsely surpass the noted Diamond search (DS) algorithm. The new Pentagon search algorithm is examined with the previously proposed Diamond search algorithm in terms of performance measure; the proposed algorithm attains better performance with the less complexity. The experimental examination also depicts that the pentagon algorithm is better than the previously proposed Diamond search (DS) in terms of mean-square error performance and required the number of search points. The overall speed improvement rate (SIR) is about 31% with respect to the DS.

A novel diamond–hexagon search algorithm for motion estimation

Motion estimation (ME) is initial way for video compression and associated to the compression efficacy by dipping temporal redundancies. ME is the most significant measure of a video encoder and half of coding intricacy or computational time depends on it. There were numerous ME algorithms suggested and realized to minimalize the computational time. H.264/AVC codec deals various coding method for realizing high compression gains as relate to other criterions. These methods affectedly rise the computational intricacy of the block based ME, which consumes up to 80% of the complete encoder’s computations. In this paper, we proposed diamond–hexagon search algorithm for block matching ME method. This proposed method delivers lessening in computational intricacy and encoding time without conceding the quality of the video sequence.

Accelerating motion estimation by genetic algorithm approach in x265

In the last two decades, in the domain of video coding and compression, researchers have suggested several techniques for computation and time reduction for motion estimation (ME). We present a motion estimation algorithm for x265 video codec, based on a deterministic initial population in the genetic algorithm (GA). GA is known for its adaptive convergence, which is motivated by the biological process of survival of the fittest. The suggested scheme is targeted for the reduction of search points (SP) in a block matching motion estimation algorithm for integer-pel in B and P frames that are set to have three reference frames. The initial population constituted in our approach is a function of pre-encoded coding units at different spatial–temporal locations of the video frames and predefined hexagonal (HEX) locations. We propose a “deterministically starting” GA (GADet), toward deployment in x256 structure. In the framework of x265 code, GADet is found to offer reduction in SP at selected classes of videos considered for experimentation. To demonstrate the effectiveness of the proposed work, results have been compared with the block-based fast-full-search algorithm and the HEX search algorithm from the reference software. Traditional GA with a randomly constituted initial population, labeled as GAStc, is also implemented and an empirical comparison is carried out with GADet. The proposed GADet framework provides reduction in motion estimation time while rendering acceptable peak signal-to-noise ratio loss and an increase in a bit rate.

Literature survey on motion estimation techniques

Motion Estimation (ME) is one of the most intensive computational operations in video compression techniques. Video compression algo-rithm utilizes numerous standards such as MPEG1, MPEG4 AND H.261, H.264. Compression performance can be increased drastically by efficient motion estimation techniques by which energy is reduced within the residual frames involved in motion compensation. In this paper literature survey of motion estimation especially considering block matching ME (Motion Estimation). In this paper, comparison is made between the already existing block matching algorithms and their limitations in motion estimation along with their applications. Many algorithms including Three Step Search (TSS), Improved Three Step Search (ITSS), New Three Step Search (NTSS), Four Step Search (4SS), Diamond Search (DS), Flexible Triangle Search(FTS), Full Search(FS), Modified Full Search(MDF) are compared and their per-formance measures are discussed in this paper.

Agent-based game theoretic model for block motion estimation and its multicore implementation

Abstract Motion estimation (ME) is one of the main tools employed for eliminating temporal redundancies in video coding. It is the most critical and time-consuming tool of the complete encoder and typically requires 60%–80% of the total computational time. Block-matching ME (BME) algorithms divide a frame into macroblocks (MB) and look for the best possible match in the reference frame. This paper introduces a novel parallel framework to speed up the BME process. This is done by introducing a novel level of parallelism within the MB. The problem of BME is cast in a non-cooperative game-theoretic setting and a distributed multi-agent system is employed to solve the problem. First, a given MB is divided into subblocks and an agent is defined for each subblock. Then, the problem is formulated as a Consensus game and our approximation of the global utility function for the MB is defined. Building on this, agents' utilities are derived so that the resulting game is a potential game. To solve the game, distributed sequential and simultaneous algorithms based on game-theoretic Best Response Dynamics (BRD) and particle swarm optimization (PSO) are presented. Each agent uses PSO as its local search engine to autonomously maximize the utility of its subblock and BRD drive the agents with minimum local communication towards the maximum of the global utility function of the whole MB. Experimental results show that these algorithms provide good estimation quality with low computational cost as compared to other techniques. Moreover, in addition to its decentralized and distributed nature, the simultaneous algorithm is also inherently parallel at the agents' level within the MB. A parallel implementation of this algorithm using the MATLAB Parallel Computing Toolbox™ (PCT) on a multicore system shows that speedup is indeed obtained.

An Improved Approach of Block Matching Algorithm for Motion Vector Estimation

Motion estimation has traditionally been used in video encoding only, however, it can also be used to solve various real-life problems. Nowadays, researchers from different fields are turning towards motion estimation. Motion estimation has become a serious problem in many video applications. It is a very important part of video compression technique and it provides improved bit rate reduction and coding efficiency. The process of motion estimation is used to improve compression quality and it also reduces computation time. Block-based motion estimation algorithms are used as they require less memory for processing of any video file. It also reduces the complexity involved in computations. In this article, various block-matching motion estimation algorithms are discussed such as Full search (FS) or Exhaust Search, Three-Step search (TSS), New Three-Step search (NTSS), Four-Step search (FSS), Diamond search (DS) etc.

Merge Mode for Deformable Block Motion Information Derivation

Motion estimation (ME) plays an important role in most video coding standards since it can improve the coding performance significantly. Block matching ME (BMME) based on translation motion model is employed in both H.264/Advanced Video Coding and High Efficiency Video Coding (HEVC), which assumes that all the pixels in a block undergo the same motion. However, BMME cannot represent the complex motion accurately, such as rotation, zoom, and deformation motion. To resolve the problem, a merge mode for deformable block motion information derivation is proposed, which considers bilinear interpolation model, six-parameter affine model, and four-parameter affine model simultaneously. The sum of absolute transformed differences criterion is used to select the best model for a block. In the proposed merge mode, the motion information of the current block is derived from the motion information of its neighbors. It does not need to perform ME and transmit motion parameters. The proposed merge mode is integrated into HEVC test model (HM) 14.0. The simulation results show that compared with HM14.0, the proposed merge mode can bring 1.3%, 1.7%, and 2.7% Bjontegaard delta (BD) bitrate savings on average for test sequences of HEVC, and 11.0%, 15.2%, and 17.4% BD bitrate savings for deformation sequences in random access main, low-delay B main, and low-delay P main test conditions, respectively, while the increased encoding and decoding complexities are moderate.

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.