Efficient Fast Motion Estimation Research Articles

Efficient fast motion estimation algorithm for real-time applications

Efficient fast motion estimation algorithm for real-time applications

Correction to: Efficient fast motion estimation algorithm for real-time applications

Correction to: Efficient fast motion estimation algorithm for real-time applications

Gradient-based edge detection for motion estimation in H.264/AVC

This study introduces a block-based motion estimation algorithm based on projection with adaptive window-size selection. Using one-dimensional (1D) projection, the original 2D block-matching problem is translated to a simpler 1D matching problem which eliminates the majority of potential pixel participation. The authors propose an efficient fast-motion estimation method using an intensity gradient algorithm to reduce the encoding time. The proposed algorithm estimates the texture direction of the block, and then a horizontal or vertical projection is employed for the sum of absolute difference calculation. Experimental results show that the proposed algorithm achieves a greater than 60% time reduction with negligible peak signal to noise ratio (PSNR) loss and BR increments.

Motion estimation using two-stage predictive search algorithms based on joint spatio-temporal correlation information

Motion estimation is one of the major problems in developing a real-time software-based video codec since it has high search complexity. In the motion estimation process, the motion field of the current block can generally be tracked from the motion fields of the neighboring blocks in the spatial and temporal directions. In this paper, two efficient fast motion estimation algorithms with a two-stage predictive search based on joint spatio-temporal correlations are proposed to reduce the search complexity. In the first stage, a rough search from the given motion vectors associated with six spatially and temporally correlated blocks attempts to find a starting point of the adequate search range that is closer to the global optimum. In the second stage, block-based gradient descent search ( Liu & Feig, 1996) and predictive partial search (proposed) algorithms are used for fine search to elaborately search the adequate range from the starting point for the best motion vector. Simulation results demonstrate that our algorithms effectively reduce the average number of checked points to only 1.55% and 0.78% as compared to the full search method and yield a great performance improvement in terms of computational complexity, PSNR and bit rates as compared to full search and some well-known fast search methods.

Region similarity based edge detection for motion estimation in H.264/AVC

This paper introduces a block-based motion estimation algorithm based on projection with adaptive window size selection. By using one-dimensional (1-D) projection the original two-dimensional (2-D) block-matching problem is translated to a simpler 1D matching, which eliminates majority of potential pixel participation. In this paper, we propose an efficient fast motion estimation method by using region similarity detection (RSD) algorithm to reduce the encoding time. The proposed RSD algorithm effectively estimates the texture direction of the block to find the direction and then horizontal, vertical or diagonal projection is employed for SAD calculation. Experimental results show that the proposed algorithm achieves more than a 60% time reduction with negligible PSNR loss and bitrate increments.