A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images
Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been propo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2010
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| Online Access: | http://psasir.upm.edu.my/id/eprint/12833/ http://psasir.upm.edu.my/id/eprint/12833/1/A%20review%20of%20wave.pdf |
| _version_ | 1848841943587487744 |
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| author | Al-Azzawi, Alaa Khamees Saripan, M. Iqbal Jantan, Adznan O. K. Rahmat, Rahmita Wirza |
| author_facet | Al-Azzawi, Alaa Khamees Saripan, M. Iqbal Jantan, Adznan O. K. Rahmat, Rahmita Wirza |
| author_sort | Al-Azzawi, Alaa Khamees |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been proposed in the past decade, particularly error concealment (EC) algorithms. In this paper, we focus on the algorithms that have high efficiency to fill-in the corrupted patches. On the other hand, we also present a new way of detecting the horizontal and vertical gradients especially, in the un-smooth patches. This improves the edge detector filter. Ultimately, a novel scheme for vertical and horizontal interpolation between the corrupted pixels and the non-corrupted adjacent pixels is achieved by improving the efficiency of filling-in. We used a new technique known as the wave-net model. This model combines the wavelet with the neural network classifier (NNC). The neural network was trained in advance to reduce the error extent for the pixels that may occur in the error. The experimental results were convincing and close to the desired. The proposed method is able to enhance image quality in term of both visual perception and the blurriness effects (BE). |
| first_indexed | 2025-11-15T07:51:16Z |
| format | Article |
| id | upm-12833 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T07:51:16Z |
| publishDate | 2010 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-128332018-10-25T06:43:35Z http://psasir.upm.edu.my/id/eprint/12833/ A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images Al-Azzawi, Alaa Khamees Saripan, M. Iqbal Jantan, Adznan O. K. Rahmat, Rahmita Wirza Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been proposed in the past decade, particularly error concealment (EC) algorithms. In this paper, we focus on the algorithms that have high efficiency to fill-in the corrupted patches. On the other hand, we also present a new way of detecting the horizontal and vertical gradients especially, in the un-smooth patches. This improves the edge detector filter. Ultimately, a novel scheme for vertical and horizontal interpolation between the corrupted pixels and the non-corrupted adjacent pixels is achieved by improving the efficiency of filling-in. We used a new technique known as the wave-net model. This model combines the wavelet with the neural network classifier (NNC). The neural network was trained in advance to reduce the error extent for the pixels that may occur in the error. The experimental results were convincing and close to the desired. The proposed method is able to enhance image quality in term of both visual perception and the blurriness effects (BE). Springer 2010-12 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/12833/1/A%20review%20of%20wave.pdf Al-Azzawi, Alaa Khamees and Saripan, M. Iqbal and Jantan, Adznan and O. K. Rahmat, Rahmita Wirza (2010) A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images. Artificial Intelligence Review, 34 (4). pp. 309-342. ISSN 0269-2821; ESSN: 1573-7462 10.1007/s10462-010-9177-7 |
| spellingShingle | Al-Azzawi, Alaa Khamees Saripan, M. Iqbal Jantan, Adznan O. K. Rahmat, Rahmita Wirza A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title | A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title_full | A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title_fullStr | A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title_full_unstemmed | A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title_short | A review of wave-net identical learning & filling-in in a decomposition space of (JPG-JPEG) sampled images |
| title_sort | review of wave-net identical learning & filling-in in a decomposition space of (jpg-jpeg) sampled images |
| url | http://psasir.upm.edu.my/id/eprint/12833/ http://psasir.upm.edu.my/id/eprint/12833/ http://psasir.upm.edu.my/id/eprint/12833/1/A%20review%20of%20wave.pdf |