Fast Block Matching Motion Estimation Research Articles

Efficient search algorithm for block motion estimation

Many suboptimal motion vector search algorithms have been proposed because the full search algorithm, which is an optimal method, requires huge computational cost. Most of these algorithms find motion vectors simply from the center of the search window. In this paper, we propose an efficient motion vector search algorithm which, in order to predict the initial search point, 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. Our proposed algorithm searches for a motion vector from this initial search point, instead of from the center of the search window, using either the diamond search algorithm [J. Y. Tham, S. Ranganath, M. Ranganath, and A. A. Kassim, ‘‘A novel unrestricted center-biased diamond search algorithm for block motion estimation,’’ IEEE Trans. Circuits Syst. Video Technol. 8(4), 369–377 (1998) and S. Zhu and K. Ma, ‘‘A new diamond search algorithm for fast block matching motion estimation,’’ in ICICS’97, pp. 9–12, Singapore (Sept. 1997)] or the unrestricted small diamond search algorithm which performs its search always with a smaller diamond search pattern. Experimental results show that our proposed algorithm is faster than other suboptimal block matching algorithms while it maintains lower average block distortion.

Simplex minimization for single- and multiple-reference motion estimation

Block-matching motion estimation (BMME) can be formulated as a 2-D constrained minimization problem. This problem can, therefore, be solved with reduced complexity using optimization techniques. This paper proposes a novel fast BMME algorithm called the simplex minimization search (SMS). The algorithm is based on the simplex minimization (SM) optimization method. The initialization procedure, termination criterion, and constraints on the independent variables of the search are designed to take advantage of the characteristics of the BMME problem and the properties of the block motion fields of typical video sequences. Simulation results show that the proposed algorithm outperforms other fast BMME algorithms, providing better prediction quality, a smoother motion field, and higher speed-up ratio. This paper also investigates the properties of the multiple-reference (MR) block motion field. Guided by the results of this investigation, the paper extends the SMS algorithm to the MR case. Three MR SMS algorithms are proposed, providing different degrees of compromise between prediction quality and computational complexity. Simulation results using 50 reference frames indicate that the proposed MR algorithms have a computational complexity comparable to that of single-reference full-search while still maintaining the prediction gain of MR motion estimation.

Correction to "A new diamond search algorithm for fast block-matching motion estimation"

Correction to "A new diamond search algorithm for fast block-matching motion estimation"

A new diamond search algorithm for fast block-matching motion estimation

Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.

Simplex minimisation for fast block matching motionestimation

The authors formulate block matching motion estimation as a two-dimensional constrained minimisation problem and then solve this problem using simplex minimisation (SM). To achieve this, a suitable initialisation procedure is proposed, in addition to a termination criterion and constraints on the independent variables of the search. The proposed algorithm is demonstrated to outperform other fast block matching motion estimation techniques.

Design of fast motion estimation algorithm based on hardware consideration

Most fast block-matching motion estimation (BMME) algorithms are designed to minimize the search positions (or checking points) in a given search range in order to speed up the computation. In this paper, we introduce a fast BMME algorithm based on the consideration of hardware implementation. We use a one-dimensional (1-D) systolic array as the basic computing engine. In order to utilize this engine efficiently, we process several adjacent checking points, called checking vector, simultaneously. We propose a checking-vector-based search strategy and show that it can achieve a better algorithmic performance and can be very cost-effective in terms of hardware implementation for low bit-rate video applications.

A high performance fast search algorithm for block matching motion estimation

In this paper, we introduce a new fast search block matching motion estimation algorithm with high performance. Based on the center-biased characteristic for most of the video sequences, the proposed algorithm can jointly minimize the error image and total number of bits spent on the motion vectors with much reduced computational complexity. Simulation results show that the proposed algorithm can produce high performance with high speed-up ratio, especially for stationary or quasi-stationary video sequences.