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A. V., Ravi Kumar
- Real-Time Video Scaling Based on Convolution Neural Network Architecture
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sir M. Visvesvaraya Institute of Technology, IN
2 Department of Electronics and Communication Engineering, SJB Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Sir M. Visvesvaraya Institute of Technology, IN
2 Department of Electronics and Communication Engineering, SJB Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 1 (2017), Pagination: 1533-1542Abstract
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.Keywords
Image Scaling, Convolution Neural, Network, Super Resolution.
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- Efficient High Quality Video Assessment Using Salient Features
Abstract Views :185 |
PDF Views:7
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sir M. Visvesvaraya Institute of Technology, IN
2 Department of Electronics and Communication Engineering, SJB Institute of Technology, IN
1 Department of Electronics and Communication Engineering, Sir M. Visvesvaraya Institute of Technology, IN
2 Department of Electronics and Communication Engineering, SJB Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 1 (2017), Pagination: 1575-1582Abstract
High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.Keywords
HEVC, AUC, NSS, Encoding.References
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