<P>This paper proposes a new frame rate up-conversion (FRUC) algorithm to reduce the computational complexity and to improve the peak signal-to-noise ratio (PSNR) performance. The proposed FRUC algorithm includes prediction-based motion vector s...
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https://www.riss.kr/link?id=A107611537
2014
-
SCI,SCIE,SCOPUS
학술저널
384-393(10쪽)
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
<P>This paper proposes a new frame rate up-conversion (FRUC) algorithm to reduce the computational complexity and to improve the peak signal-to-noise ratio (PSNR) performance. The proposed FRUC algorithm includes prediction-based motion vector s...
<P>This paper proposes a new frame rate up-conversion (FRUC) algorithm to reduce the computational complexity and to improve the peak signal-to-noise ratio (PSNR) performance. The proposed FRUC algorithm includes prediction-based motion vector smoothing (PMOS), partial average-based motion compensation (PAMC), and intrapredicted hole interpolation (IPHI). PMVS can efficiently remove outliers using motion vectors of neighboring blocks and PAMC performs motion compensation with the region-based partial average to reduce blocking artifacts of the interpolated frames. For hole interpolation, IPHI uses intraprediction of H.264/AVC to eliminate blurring and also uses the fixed weights implemented using only shift operations, which result in low computational complexity. Compared to the existing algorithms, which use bilateral motion estimation, the proposed algorithm improves the average PSNR of the interpolated frames by 3.44 dB and lowers PSNR performance only by 0.13 dB than the existing algorithm that employs unilateral ME; however, it can significantly reduce the computational complexity of FRUC about 89.3% based on the absolute difference.</P>
Image-Optimized Rolling Cache: Reducing the Miss Penalty for Memory-Intensive Vision Algorithms