Matching Motion Estimation Research Articles

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.

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.

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.

A Pattern-Based Artificial Bee Colony Algorithm for Motion Estimation in Video Compression Techniques

Block matching (BM) motion estimation plays an inevitable role in video coding applications. BM approaches are used for data compression. The compression is achieved by removing the temporal redundancy in the video sequences. In the BM process, each video frame is subdivided into macroblocks. Each macroblock in the current frame is compared with the previous frame. The main objective is to minimize sum absolute difference. In this work, some modifications have been performed on conventional artificial bee colony algorithm to improve the conventional BM systems. An initial pattern is used in the proposed algorithm to reduce the computational cost. The computational cost is represented in terms of search points and convergence time. Experimental results results show the improvement for the proposed approach over other block matching algorithms in terms of the performance measures.

High Performance Architecture of Motion Estimation Algorithm for Video Compression

Motion estimation (ME) is a highly computationally intensive operation in video compression. Efficient ME architectures are proposed in the literature. This paper presents an efficient low computational complexity systolic architecture for full search block matching ME (FSBME) algorithm. The proposed architecture is based on one-bit transform-based full search (FS) algorithm. The proposed ME hardware architectures perform FS ME for four macroblocks (MBs) in parallel. The proposed hardware architecture is implemented in VHDL. The FSBME hardware consumes 34% of the slices in a Xilinx Vertex XC6vlx240T FPGA device with a maximum frequency of 133[Formula: see text]MHz and is capable of processing full high definition (HD) ([Formula: see text]) frames at a rate of 60 frames per second.

A hierarchical algorithm with fast convergence spiral search pattern for block matching in motion estimation

In a video encoding process, motion estimation (ME) has an important effect, because it approximately takes 60% to 80% of computation in the total computation complexity of video coding. ME is usually applied to reduce the redundancy that exists between successive frames of a video sequence. The motion vector (MV) obtained through this process represent the movement of objects between the frames. One of the most efficient and well-known ME techniques is block matching algorithm (BMA). BMAs have been widely used in several video coding standards. There are various BMAs which try to reduce the number of computations in order to speed up the video encoding process. Here, a block matching ME algorithm called hierarchical algorithm with fast convergence spiral search (HAFCSS) is developed with the intention of reducing the computational complexity while maintaining the visual quality. This is implemented in MATLAB with different video sequences having different motion content. The proposed method provides a better speed improvement of 9% to 45% with reduced number of search points.

Low-power systolic array processor architecture for FSBM video motion estimation

A novel power-efficient systolic array architecture is proposed for full search block matching (FSBM) motion estimation, where the partial distortion elimination algorithm is used to dynamically switch off the computation of eliminated partial candidate blocks. The RTL-level simulation shows that the proposed architecture can reduce the power consumption of the computation part of the algorithm to about 60% of that of the conventional 2D systolic arrays.

Cost-adaptive motion estimation strategy for high-performance video encoders

An adaptive cost block matching (ACBM) motion estimation technique is proposed to achieve a good compromise between the visual quality of decoded video sequences and the computational cost. Simulation results demonstrate that the ACBM algorithm is able to obtain a better rate-distortion performance that the one given by the full-search algorithm, with reductions of up to 90% in the computational cost.

One-dimensional dense disparity estimation for three-dimensional reconstruction

We present a method for fully automatic three-dimensional (3D) reconstruction from a pair of weakly calibrated images in order to deal with the modeling of complex rigid scenes. A two-dimensional (2D) triangular mesh model of the scene is calculated using a two-step algorithm mixing sparse matching and dense motion estimation approaches. The 2D mesh is iteratively refined to fit any arbitrary 3D surface. At convergence, each triangular patch corresponds to the projection of a 3D plane. The proposed algorithm relies first on a dense disparity field. The dense field estimation modelized within a robust framework is constrained by the epipolar geometry. The resulting field is then segmented according to homographic models using iterative Delaunay triangulation. In association with a weak calibration and camera motion estimation algorithm, this 2D planar model is used to obtain a VRML-compatible 3D model of the scene.

FAST MOTION ESTIMATION SCHEME FOR VIDEO CODING USING FEATURE VECTOR MATCHING AND MOTION VECTOR'S CORRELATIONS

In this paper, we propose a fast block motion estimation scheme by exploiting the correlations of motion vectors (MV) existing in spatially- and temporally-adjacent as well as hierarchically-related blocks. The basic idea is to use the information obtained from the corresponding block at a coarser resolution level and spatio-temporal neighboring blocks at the same level in order to select a good set of initial MV candidates and then perform further local search to find the best matching MV. In order to further reduce computational complexity, the sign pattern vector (SPV) is defined and used for block matching, as opposed to the pixel intensity values used in the conventional block matching methods. By using the SPV definition, a data block can be presented by a mean and a set of binary sign patterns with a much reduced data set. The block matching motion estimation is then divided into mean matching and binary phase matching. The proposed technique enables a significant reduction in computational complexity compared with the conventional block matching motion estimation (ME) because binary phase matching only involves Boolean logic operations. This scheme also significantly reduces the data transfer time between the frame buffer and motion estimator. Our test results indicate that the performance of the proposed scheme is comparable with the full-search block matching under the same search ranges, however, the proposed scheme has a speed-up factor ranging from 250 to 370 in comparison with full search.

A fast hierarchical motion-compensation scheme for video coding using block feature matching

This paper presents a fast hierarchical feature matching-motion estimation scheme (HFM-ME) that can be used in H.263, H.261, MPEG 1, MPEG 2, and HDTV applications. In the HFM-ME scheme, the sign truncated feature (STF) is defined and used for block template matching, as opposed to the pixel intensity values used in conventional block matching methods. The STF extraction process can be considered as a zero-crossing phase detection with the mean as the bias and binary sign pattern as the phase deviation. Using the STF definition, a data block can be represented by a mean and a set of binary features with a much reduced data set. The block matching motion estimation is then divided into mean matching and binary phase matching. The proposed technique enables a significant reduction in computational complexity compared with the conventional full-search block matching ME because binary phase matching only involves Boolean logic operations. This feature also significantly reduces the data transfer time between the frame buffer and motion estimator. The proposed HFM-ME algorithm is implemented and compared with the conventional full-search block matching schemes. Our test results using three full-motion MPEG sequences indicate that the performance of the HFM-ME is comparable with the full-search block matching under the same search ranges, however, HFM-ME can be implemented about 64 times faster than the conventional full-search schemes. The proposed scheme can be combined with other fast algorithms to further reduce the computational complexity, at the expense of picture quality.