An Adaptive Scheme to Achieve Fine Grained Video Scaling

S Safinaz, A. V. Ravi Kumar

Abstract


A robust Adaptive Reconstruction Error Minimization Convolution Neural Network ( ARemCNN) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.


Keywords


PSNR, ARemCNN, YUV21, Reconstruction Quality, CNN

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DOI: http://doi.org/10.11591/ijeecs.v8.i1.pp43-58

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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