Abstract
Image compression helps in storing the transmitted data in proficient way by decreasing its redundancy. This technique helps in transferring more digital or multimedia data over internet as it increases the storage space. This research presents some methods to compress digital images using Artificial Intelligence Techniques(AITs) that include from fuzzy logic, swarm intelligent technique, and artificial neural networks. Traditional clustering algorithm k-means and AITs were used, such as Gath-Geva fuzzy clustering algorithm, and Particle Swarm Optimization Technique(PSO), and combined Gath-Geva with backpropagation neural network to produce a new method which is called Fuzzy BackPropagation Network (FBPN) algorithm, by applying these methods on gray level and color images and then applying compression algorithm RLE on it to obtain compressed image. Image quality measures have done by Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), and Bitperpixel(bpp), compression ratio (CR) have been computed. Finally, a comparison between results after applying these algorithms on the images data set was obtained.
Highlights
Digital image presentation requires a large amount of data and its transmission over communication channels is time consuming
Compression was measured in bits per pixel(BPP), and compression ratio was calculated by this equation[18]: compression ratio = uncompressed file size compressed file size
The computed values for bpp, peak signal-to-noise ratio (PSNR), and compression ratio(CR)for color images pepperd_red, strawberry, and for gray level tulips image using these four methods which are given in tables 1- 3
Summary
Digital image presentation requires a large amount of data and its transmission over communication channels is time consuming. K-means is one of the common partitioning techniques which has large number of applications in the fields of image and video compression, image segmentation, pattern recognition and data mining. Fuzzy systems have been successfully applied to various areas such as classification, simulation, data mining, pattern recognition, image compression[4], Gath-Geva (G-G) fuzzy clustering algorithm takes the size and density of the clusters into account[5]. The ensembles of interconnected artificial neurons generally organized into layers of fields include neural networks. The behavior of such ensembles varies greatly with changes in architectures as well as neuron signal functions [8]. In this research was combined Gath-Geva fuzzy clustering method with backpropagation neural network to produce Fuzzy Backpropagation Neural Network (FBPNN)
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