Neural networks for fast image compression /
algorithm was adopted. In order to keep the
| Main Author: | |
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1998.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy |
| Summary: | algorithm was adopted. In order to keep the and decrease the number of neurons in the intermediate aspects: compression ratio, image distortion, and based on the predictive values and the input vectors. compress a gray-scale image. The system consists of compression part includes the input layer and the compression ratio. Moreover, the parallel architecture consists of the intermediate layer and the output converge, an adaptive back-propagation learning design of an image compression system involves three fidelity of the image, such that at the receiver, the for the learning process of the neural networks to generalization capability of the compression system good Peak-to-peak Signal to Noise Ratio and high image, a set of natural networks instead of one implement a new compression/decompression system to inherent in the architecture of the neural networks intermediate layer, while the decompression part layer, a preprocessing element is designed which layer. To gain high quality of the reconstructed makes the compression system process very quickly. network have been used in the system. Each neural network was trained with some image blocks which have number of bits transmitted as well as keeping the performs the necessary' processing of the image before predictor which predicts the current input block and a processing speed. In this thesis, we design and reconstructed image will have little distortion. The similar characteristics. In order to decrease the time subtracting element which generates residual vectors The final results shows that the reconstructed image The image compression system aims at reducing the graphics. the image is encoded. The preprocessor includes a three-layer feed forward neural networks. The which was processed by our proposed scheme had a very |
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| Item Description: | "Major subject: Electrical Engineering". Vita. |
| Physical Description: | x, 62 leaves : illustrations ; 28 cm. Also available online. |
| Bibliography: | Includes bibliographical references (leaves 42-44). |