Fast Block Motion Estimation Algorithm Research Articles

A New Fast Block Motion Algorithm Based on Octagon and Triangle Search Patterns for H.264/AVC

The block motion estimation process is an integral and important part in the current video coding standards. However, the current algorithms for the block motion estimation process either have a large computational complexity, or have a poor accuracy. The UMHexagonS algorithm is recommended for the implementation of H.264 for its relatively good performance. In this paper, a new fast block motion estimation algorithm is proposed, which reduces the computational complexity of the UMHexagonS algorithm by more than 33 percent and have about the same accuracy. This is achieved by first classifying the motion type of a block. Then, for the block with regular and small motion, two newly proposed efficient search patterns, the octagon search pattern and the triangle search pattern, are, respectively and recursively, utilized to find a good motion vector by using low computational complexity. For the block with irregular or large motion, a symmetrical cross search pattern is first used, followed by the multi-octagon-grid search pattern due to its low complexity and good search efficiency, and finally the large diamond and triangle search pattern are, respectively and recursively utilized to refine the search process and obtain a motion vector with good accuracy.

Estimation of Motion Vector Parameters Using Modified Diamond Search

A fast block motion estimation algorithm is proposed using modified diamond search patterns. This algorithm utilizes the directions and magnitudes of motion vectors between interblocks and uses a smaller number of search points than conventional diamond search patterns. Simulation results show that the proposed method significantly improves computational speed over other fast motion estimation algorithms without degradation of distortion.

Novel Directional Gradient Descent Searches for Fast Block Motion Estimation

Search point pattern-based fast block motion estimation algorithms provide significant speedup for motion estimation but usually suffer from being easily trapped in local minima. This may lead to low robustness in prediction accuracy particularly for video sequences with complex motions. This problem is especially serious in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS), which provide very high speedup ratio. A multipath search using more than one search path has been proposed to improve the robustness of BBGDS but the computational requirement is much increased. To tackle this drawback, a novel directional gradient descent search (DGDS) algorithm using multiple OTSs and gradient descent searches on the error surface in eight directions is proposed in this letter. The search point patterns in each stage depend on the minima found in these eight directions, and thus the global minimum can be traced more efficiently. In addition, a fast version of the DGDS (FDGDS) algorithm is also described to further improve the speed of DGDS. Experimental results show that DGDS reduces computation load significantly compared with the well-known fast block motion estimation algorithms. Moreover, FDGDS can achieve faster speedup compared with the UMHexagonS algorithm in H.264/AVC implementation while maintaining very similar rate-distortion performance.

Fast block motion estimation algorithm based on motion classification and directional search patterns

This paper suggests a simple scheme of block motion estimation in which the search pattern is divided into a number of sectors. By employing prediction the motion vector search is constrained to a small area. In the first step five neighboring blocks are searched for finding the predicted motion vector. The vector thus obtained is chosen as the initial search center. This predictive search center is closer to the global minimum and thus decreases the effect of the monotonic error surface assumption on the motion field estimates. Secondly, the prediction information is used to obtain the direction of the predicted motion vector. Based on that direction, the search area is divided into eight sectors. Thirdly, the magnitude of the predicted motion vector is used to define the motion content of different blocks. Thus the final search pattern dynamically uses the motion magnitude and motion direction information and significantly reduces the computational complexity. Experimental results show speed improvement of the proposed algorithm over other fast search algorithms; in addition, the image quality measured in terms of peak SNR also shows good results.

Fast Multi-reference Motion Estimation via Enhanced Downhill Simplex Search

Block motion estimation can be regarded as a function minimization problem in a finite-dimensional space. Therefore, fast block motion estimation can be achieved by using an efficient function minimization algorithm instead of a predefined search pattern, such as diamond search. Downhill simplex search is an efficient derivative-free function minimization algorithm. In this paper, we proposed a fast block motion estimation algorithm based on applying the downhill simplex search for function minimization. Several enhanced schemes are proposed to improve the efficiency and accuracy, including a new initialization process, a special rounding scheme, and an early-stop error function evaluation procedure. We also extend the downhill simplex search for the multi-reference frame motion estimation problem. Experimental results show superior performance of the proposed algorithm over some existing fast block matching methods on several benchmarking video sequences.

Fast motion estimation using modified diamond search patterns

A fast block motion estimation algorithm is proposed using modified diamond search patterns. This algorithm utilises the directions and magnitudes of motion vectors between interblocks and uses a smaller number of seach points than conventional diamond search patterns. Simulation results show that the proposed method significantly improves computational speed over other fast motion estimation algorithms without degradation of distortion.

A Novel Fast Block Motion Estimation Algorithm Based on Combined Subsamplings on Pixels and Search Candidates

Block motion estimation is one of the key technologies in video compression and has been widely adopted by several existing international standards of video coding. Many popular block motion estimation methods, including three-step search (TSS), new three-step search, and four-step search, have assumed that the error surface is unimodal over the search area or the motion vector is center-biased. However, these assumptions do not hold for most MPEG-1,2 video frames. As a result, schemes with these assumptions will exhibit degraded performance as they are applied to MPEG-1,2 video frames. In this paper, we propose a novel fast block motion estimation scheme based on an integration of pixels subsampling and search candidates subsampling. Compared with the well-known TSS algorithm, the proposed scheme visits more candidates so that it can significantly avoid being trapped into the local minima and therefore is more robust. Experimental results using typical MPEG-1 video frames show that the proposed algorithm can achieve better PSNR as well as higher speed-up ratios than the well-known TSS. In addition, the combined subsampling scheme has a regular structure that facilitates easy hardware implementation.

Partial norm based search algorithm for fast motionestimation

A fast block motion estimation algorithm is presented which uses the sum of partial norms that can obtain the same motion vectors as the exhaustive full search algorithm but with a reduced computational load. Experimental results show that the proposed algorithm is ~3–4 times faster than the original exhaustive full search method, and is almost twice as fast as the successive elimination algorithm (SEA).

Optimization of fast block motion estimation algorithms

There are basically three approaches for carrying out fast block motion estimation: (1) fast search by a reduction of motion vector candidates; (2) fast block-matching distortion (BMD) computation; and (3) motion field subsampling. The first approach has been studied more extensively since different ways of reducing motion vector candidates may result in significantly different performance; while the second and third approaches can in general be integrated into the first one so as to further accelerate the estimation process. In this paper, we first formulate the design of good fast estimation algorithms based on motion vector candidate reduction into an optimization problem that involves the checking point pattern (CPP) design via minimizing the distance from the true motion vector to the closest checking point (DCCP). Then, we demonstrate through extensive studies on the statistical behavior of real-world motion vectors that the DCCP minimization can result in fast search algorithms that are very efficient as well as highly robust. To further utilize the spatiotemporal correlation of motion vectors, we develop an adaptive search scheme and a hybrid search idea that involves a fixed CPP and a variable CPP. Simulations are performed to confirm their advantages over conventional fast search algorithms.