A complexity-bounded motion estimation algorithm

Abstract

The full search motion estimation algorithm for video coding is a procedure of high computational cost. For this reason, in real-time low-power applications, low-cost motion estimation algorithms are viable solutions. A novel reduced complexity motion estimation algorithm is presented. It conjugates the reduction of computational load with good encoding efficiency. It exploits the past history of the motion field to predict the current motion field. A successive refinement phase gives the final motion field. This approach leads to a sensible reduction in the number of motion vector that have to be tested. The complexity is lower than any other algorithm algorithms known to the authors, in the literature, it is constant as there is no recursivity in the algorithm and independent of any search window area size. Experimental evaluations have shown the robustness of the algorithm when applied on a wide set of video sequences--a good performance compared to other reduced complexity algorithms and negligible loss of efficiency versus the full search algorithm.

Motion Estimation Using Spatio-Temporal Contextual Information

“…With this assumption, instead of calculating all the possible motion MVs as the full search does, the previously calculated MVs for the MBs that are spatially and temporally contiguous, can be used as the candidate predictors for the current block. The algorithm proposed by Antonio et al, consists of two spatial predictors belonging to the same frame, two temporal predictors belonging to the previous frame and a null vector [6]. The best predictor (the MV with the minimum SAD), is sent to a refinement phase that allows to obtain the final motion vector.…”

“…Inaccurate and noisy MVs reduce the maximum achievable compression ratio and the quality of the reconstructed video. It is possible to obtain smooth MVs by using the motion estimates previously obtained in the spatio-temporal neighborhood of a block before starting the search [3], [6]. The goal is to make sure that the MVs within a small neighborhood are consistent.…”

“…In this section, we propose a solution to obtain smooth and consistent MVs within a small region. The proposed approach is easier and computationally less expensive to implement than the schemes proposed in the literature for the same purpose [3], [6].…”

Probability Based Search Motion Estimation Algorithm Using Mean Correction

“…The only disadvantage of this method and perhaps the biggest flaw is the high computational cost associated with it. Other algorithms with reduced number of computations, for example, the successive elimination algorithm (SEA) [4], a new three step search [5], a novel four-step search (FSS) [6], efficient four step search [7], unrestricted center biased diamond search (UCBDS) [8], cross search [9], fast full search motion estimation [10], complexity bounded motion estimation [11], new fast algorithm for estimation of block motion vector [12], dynamic search window algorithm [13], predictive coding based on interframe efficient motion estimation [14], displacement measurement and its application in image coding [15], a new efficient block-matching algorithm for motion estimation [16], fast variable block-size motion estimation algorithms based on merge and split procedure [17], Among these algorithms, the SEA is similar to the full search method except, the first one eliminates certain search points based on the Minkowiski's inequality. Further reduction in number of search points was achieved in TSS algorithm which starts with a step having nine uniformly spaced search points which get closer after every step until the step size reduces to 1.…”

A 3D Spatio-Temporal Motion Estimation Algorithm for Video Coding

“…Research of decades focuses on reducing the complexity of ME and maintaining the performance as much as possible. Comprehensive surveys are available in [1] [2].…”