Abstract
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.
Highlights
Frame interpolation, a technique to upconvert the video frame rate from a lower one into a higher one, has been recognized as important since the advent of television standards (e.g., NTSC and PAL) having different frame rates (Thomas 1987; de Haan 2000)
Several motion compensated frame interpolation (MCFI) algorithms have been proposed in recent years (Hsu and Chien 2008; Qian and Bajic 2013; Ponla et al 2009; Mahajan et al 2009; Han and Woods 1997; Kim and Sunwoo 2014; Wang et al 2010; Ha et al 2004; Gunyel and Alatan 2010; Choi et al 2007; Kang et al 2008, 2010), and they can be classified into three types with respect to motion estimation (ME)
Experimental results we describe various experiments conducted for performance comparisons between the proposed MCFI algorithm and existing algorithms (Han and Woods 1997; Kim and Sunwoo 2014; Choi et al 2007; Kang et al 2010)
Summary
A technique to upconvert the video frame rate from a lower one into a higher one, has been recognized as important since the advent of television standards (e.g., NTSC and PAL) having different frame rates (Thomas 1987; de Haan 2000). ME calculates motion vectors (MVs) in moving images, and MC generates a new interpolated frame using the MVs. Several MCFI algorithms have been proposed in recent years (Hsu and Chien 2008; Qian and Bajic 2013; Ponla et al 2009; Mahajan et al 2009; Han and Woods 1997; Kim and Sunwoo 2014; Wang et al 2010; Ha et al 2004; Gunyel and Alatan 2010; Choi et al 2007; Kang et al 2008, 2010), and they can be classified into three types with respect to ME.
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