This paper presents a novel motion compensated frame interpolation (MCFI) algorithm that includes texture-based wedgelet partitioning (TWP) and multiple prediction based search (MPS). TWP partitions a rectangular block into two wedge-shaped sub-blocks using the texture information, which makes a better approximation for an actual object region. Thus, detailed motions around the object boundaries can be more precisely represented than by existing MCFI algorithms. To reliably estimate the actual motion, the MPS algorithm is used in addition to TWP. MPS considers the distances between the predicted motion vectors and the candidate motion vectors, as well as the matching error. Experimental results reveal that the proposed MCFI can improve the average peak signal-to-noise ratio performance by up to 2.93 dB compared to existing MCFIs. On the average structural similarity metric, the proposed MCFI algorithm is superior to existing algorithms by a value of up to 0.0256. In addition, the proposed MCFI can reduce the computational complexity by as much as 66.9 % with respect to the sum of absolute difference compared with existing MCFIs.
Keywords: Block matching algorithm (BMA); Distributed video coding (DVC); Frame interpolation; Frame rate up-conversion (FRUC); Motion estimation (ME).