High accuracy block-matching sub-pixel motion estimation through detection of error surface minima

Abstract

The present paper focuses on high-accuracy block-based sub-pixel motion estimation utilizing a straightforward error minimization approach. In particular, the mathematics of bilinear interpolation are utilized for the selection of the candidate motion vectors that minimize the error criterion, by estimating local minima in the error surface with arbitrary accuracy. The implemented approach favors optimum accuracy over computational load demands, making it ideal as a benchmark for faster methods to compare against; however, it is not best suited to real-time critical applications (i.e. video compression). Other video processing needs relying on motion vectors and requiring high-resolution/accuracy can also take advantage of the proposed solution (and its simplified nature in terms of underlying theoretical complexity), such as motion-compensation filtering for super resolution image enhancement, motion analysis in sensitive areas (e.g. high-speed video monitoring, medical imaging, motion analysis in sport science, big-data visual surveillance, etc.). The proposed method is thoroughly evaluated using both real video and synthetic motion sequences from still images, adopting well-tested block-based motion estimation evaluation procedures. Assessment includes comparisons to a number of existing block-based methods with respect to PSNR and SSIM metrics over ground-truth samples. The conducted evaluation takes into consideration both the original (arbitrary-accuracy) and the truncated motion vectors (after rounding them to the nearest half, quarter, or eighth of a pixel), where superior performance with more accurate motion vector estimation is revealed. In this context, the degree to which sub-pixel motion estimation methods actually produce sub-pixel motion vectors is investigated, and the implications thereof are discussed.