An Adaptive Scheme to Achieve Fine Grained Video Scaling
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 experiment...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-10-01.
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| Online Access: | Get fulltext |
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| LEADER | 02138 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs8313_7619 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Safinaz, S |e author |
| 100 | 1 | 0 | |a Dr.A.V.Ravi kumar,Sjbit, VTU, Bangalore, India |e contributor |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a V. Ravi Kumar, A. |e author |
| 245 | 0 | 0 | |a An Adaptive Scheme to Achieve Fine Grained Video Scaling |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8313 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Asst. Professor; Dept. of Electronics and Communications; | ||
| 690 | |a PSNR, ARemCNN, YUV21, Reconstruction Quality, CNN | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 8, No 1: October 2017; 43-58 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v8.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8313/7619 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8313/7619 |z Get fulltext |