Motion estimation based on spatio-temporal correlations and pixel decimation

Abstract

The full search block-matching algorithm is the simplest, but computationally very intensive approach. In recent years there was substantial progress in block motion estimation algorithms. Important milestones on this path were such algorithms as two-dimensional logarithmic search, three-step search, four step search, and diamond search. All these methods try to minimize the amount of search points applying sum of absolute differences (SAD) or equivalent metrics for each point. There were some works that studied partial SAD (PSAD) but mostly concentrated on a constant decimation factor. What we tried to study in this work is the performance of one of the best block matching search algorithms in combination with adaptive PSAD as a matching metric. The idea is that we use original motion estimation based on spatio-temporal correlation method, but instead of using SAD as a matching metric we use PSAD with adaptively chosen decimation factor. Our simulation results show that for high motion sequences PSNR degradation between full search and the proposed method was around 0.1-0.7 dB. The computational complexity reduction of 650-1700 times (compared with the full search) and 9 times (compared to the original method) is pretty big and may be well worth this video quality decrease. In the case of more static sequences PSNR degradation between full search and the original motion estimation method was around 0 dB. When we compare the original method with the proposed one the degradation increases to 0.1 dB. The computational complexity reduction was around 1600-1700 times (compared with the full search) and 5-7 times (compared to the original method).