Fast hierarchical wavelet-domain motion estimation for arbitrarily shaped visual objects

Abstract

Motion estimation is widely used for video compression as it reduces the memory requirements of any video file while maintaining high visual quality. But the computational cost consumed by motion estimation/compensation usually accounts for more than 50%, even to 80%, of the total cost of the encoder. So researchers try to develop fast motion estimation algorithms to lower the computational complexity. Moreover, few studies have been concentrated on the efficient motion estimation of arbitrarily shaped visual objects, which particularly pays insufficient attention to boundary continuity of the motion-compensated objects as well. In this research, we present a hierarchical wavelet-domain approach to motion estimation of visual objects with arbitrary shapes. We explore the guiding role of binary alpha-plane in assisting motion estimation of visual objects, and bring forward a binary alpha-plane matching scheme. It constrains the intensity-based motion estimation into a small subset of initial candidate vectors in the search window. This both accelerates search speed and helps to produce coherent object boundary. Moreover, we employ the low-band-shift technique to eliminate the wavelet’s shift-variance effect. And based on the observation that high frequency subbands of wavelet contribute little to calculating motion vectors, a new matching function is derived for fast wavelet-domain motion estimation. Coupled with the alpha-plane matching and the devised matching function, a hierarchical low-band-based motion estimation approach is further proposed. Through executing a refined search around the initial candidate vectors using only the low frequency subband at each scale, our approach achieves a satisfying balance between prediction accuracy and computational efficiency. Experimental results show that by using the standard accuracy measurements and the benchmark video sequences, the performance of the proposed approach is close to the full search with a matching quality over 99%, and with 95.80% reduction in complexity.