Video Frame Interpolation On Msu Video Frame

평가 지표

LPIPS

MS-SSIM

PSNR

SSIM

VMAF

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	LPIPS	MS-SSIM	PSNR	SSIM	VMAF
video-frame-interpolation-with-densely	0.021	0.961	29.45	0.949	72.12
video-frame-interpolation-with-transformer	0.044	0.942	28.34	0.917	68.87
xvfi-extreme-video-frame-interpolation	0.061	0.933	27.35	0.913	63.47
enhanced-bi-directional-motion-estimation-for	0.024	0.958	28.77	0.931	68.20
learning-spatial-transform-for-video-frame	0.058	0.913	24.99	0.903	60.19
a-unified-pyramid-recurrent-network-for-video	0.025	0.962	29.73	0.951	71.34
featureflow-robust-video-interpolation-via	0.060	0.911	24.48	0.902	60.70
featureflow-robust-video-interpolation-via	0.070	0.894	23.28	0.889	58.11
bmbc-bilateral-motion-estimation-with-1	0.071	0.898	23.34	0.885	59.27
ifrnet-intermediate-feature-refine-network	0.037	0.943	28.04	0.921	66.98
ifrnet-intermediate-feature-refine-network	0.049	0.931	27.45	0.908	63.43
video-frame-interpolation-via-adaptive	-	-	26.36	-	-
rife-real-time-intermediate-flow-estimation	0.039	0.939	27.15	0.914	66.33
asymmetric-bilateral-motion-estimation-for	0.039	0.945	27.99	0.919	68.10
xvfi-extreme-video-frame-interpolation	0.049	0.955	27.86	0.921	67.25
video-frame-interpolation-via-residue	0.072	0.902	25.76	0.893	59.82
cdfi-compression-driven-network-design-for	0.051	0.926	26.99	0.908	61.72
learning-cross-video-neural-representations	0.029	0.946	28.01	0.920	67.07
film-frame-interpolation-for-large-motion	0.033	0.948	28.11	0.928	68.68
extracting-motion-and-appearance-via-inter	0.022	0.965	29.89	0.953	71.71
learning-spatial-transform-for-video-frame	0.692	0.883	23.17	0.891	58.29
super-slomo-high-quality-estimation-of	0.068	0.924	26.69	0.904	61.35
ifrnet-intermediate-feature-refine-network	0.048	0.932	27.67	0.909	64.16
enhanced-bi-directional-motion-estimation-for	0.028	0.957	28.56	0.928	69.37