vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff3632240000008a22000001000a00 In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network) shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.
S Safinaz1, A V Ravi Kumar2
Sir M. Visvesvaraya Institute of Technology, India1, SJB Institute of Technology, India2
Image Scaling, Convolution Neural, Network, Super Resolution
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 8 , Issue: 1 , Pages: 1533-1542 )
Date of Publication :
August 2017
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572
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