05-05-2017, 09:24 AM
please put the code for "Fractal Image Compression using PSO"
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fractal image compression using particle swarm optimization in matlab
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please put the code for "Fractal Image Compression using PSO"
05-05-2017, 11:02 AM
The fractal technique is based on the similarities of oneself in the image. The main objective is to reduce coding time using a technique called BIT, based on the histogram characteristics to reduce the redundancy information in an image, which leads to the reduction of the number of blocks of rank .
The need for images in our daily lives increased drastically. This gives more attention to the compression of an image. Image compression focuses on the problem of optimizing the storage space and transmission of an image. The research advances in fractal image compression that focuses on computationally efficient and effective algorithm. Fractal compression is an asymmetric process that takes more time to compress an image than to decompress it. Explore the self-similarity property to find the best match within the image itself. In this thesis, an effect is made towards the partitioning methods and coding efficiency in terms of search time. In the existing system, the fractal image compression using a genetic algorithm with the ranking selection mechanism is used.
This algorithm applies to both fractal and non-fractal images and the result shows that the coding time for both types of images is greatly reduced while maintaining their quality. However, the encoding process is not simple and fails to maintain other types of images in terms of image quality. In this study, a new advanced method of particle swarm optimization with FIC classification is proposed to accelerate the coding process and retain the quality of recovered images. In the proposed algorithm, the image coefficients are extracted to classify the image using DCT. Then, according to the coefficients of the range regions, the search strategy for each range block is determined using an algorithm. This proposed algorithm is applied in both fractal and non-fractal images and the new result shows that the coding time for both types of images is greatly reduced and also maintains the compression ratio with the PSNR value.
The need for images in our daily lives increased drastically. This gives more attention to the compression of an image. Image compression focuses on the problem of optimizing the storage space and transmission of an image. The research advances in fractal image compression that focuses on computationally efficient and effective algorithm. Fractal compression is an asymmetric process that takes more time to compress an image than to decompress it. Explore the self-similarity property to find the best match within the image itself. In this thesis, an effect is made towards the partitioning methods and coding efficiency in terms of search time. In the existing system, the fractal image compression using a genetic algorithm with the ranking selection mechanism is used.
This algorithm applies to both fractal and non-fractal images and the result shows that the coding time for both types of images is greatly reduced while maintaining their quality. However, the encoding process is not simple and fails to maintain other types of images in terms of image quality. In this study, a new advanced method of particle swarm optimization with FIC classification is proposed to accelerate the coding process and retain the quality of recovered images. In the proposed algorithm, the image coefficients are extracted to classify the image using DCT. Then, according to the coefficients of the range regions, the search strategy for each range block is determined using an algorithm. This proposed algorithm is applied in both fractal and non-fractal images and the new result shows that the coding time for both types of images is greatly reduced and also maintains the compression ratio with the PSNR value.
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