Motion estimation using a one-dimensional gradient descent search

This work presents a low-complexity high-performance motion estimation method using a one-dimensional (1-D) gradient descent search. The proposed method consists of initial-point determination, initial-direction determination, a gradient descent search using variable step sizes and conjugate directions, and convergence checking. For each block, one of the original and predictive points with the best block-matching performance is the initial point where the predictive ones are motion vectors of its neighboring four searched blocks. In addition, the searching path is optimized by determining the initial direction based on the vector from the original point to the initial point. After determining the initial point and direction, the gradient descent search using variable step sizes and conjugate directions is performed until the searched point attains a better performance than its neighboring points. Moreover, variable step sizes for moving and slowly moving, or stationary blocks, are well addressed to improve the convergence performance of a gradient descent search. Simulation results demonstrate that our method yields a superior performance in terms of computational complexity and picture quality as compared to the three-step search, 1-D full search, block-based gradient descent search, and one-at-a-time search methods