Artificial neural network for image compression

Sicuranza, Giovanni L.; Ramponi, Giovanni; Marsi, Stefano

doi:10.1049/el:19900310

Cited by 49 publications

(21 citation statements)

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“…A neural network, in general, is a highly interconnected network of a large number of processing elements called neurons in an architecture inspired by the brain [4] [10]. Artificial neural networks are massively parallel adaptive networks of simple nonlinear computing elements called neurons which are intended to abstract and model some of the functionality of the human nervous system in an attempt to partially capture some of its computational strengths [13] [14].…”

Section: Neural Networkmentioning

confidence: 99%

“…By compressing the images, users find the storage media much more voluminous. So Image Compression enhances the progress of the world in communication [4] [7] [11].…”

Section: A Artificial Neuronmentioning

confidence: 99%

“…Step 3: The pair values (P) obtained from the above step can be represented in sequence order (S) [4].…”

Section: A Image Compression Processmentioning

confidence: 99%

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Neural Network Technique for Lossless Image Compression Using X-Ray Images

Senthilkumaran¹,

Suguna²

2011

IJCEE

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Abstract-Neural Networks are based on the parallel architecture and inspired from human brains. Neural networks are a form of multiprocessor computer system, with simple processing elements, a high degree of interconnection, simple scalar messages and adaptive interaction between elements. One such application is image compression. Image compression is a process which minimizes the size of an image file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the internet or downloaded from web pages. This paper proposes an Improved Backpropagation Neural Network Technique, for lossless image compression. The system also proves that the improved Backpropagation Neural Network Technique works better than the existing Huffman Coding Technique for lossless image compression by considering X-Ray images based on three metrics such as compression ratio, transmission time and compression performance. Experimental results are presented and compared.

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Section: Neural Networkmentioning

confidence: 99%

“…By compressing the images, users find the storage media much more voluminous. So Image Compression enhances the progress of the world in communication [4] [7] [11].…”

Section: A Artificial Neuronmentioning

confidence: 99%

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Neural Network Technique for Lossless Image Compression Using X-Ray Images

Senthilkumaran¹,

Suguna²

2011

IJCEE

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“…By compressing the images, users find the storage media much more voluminous. So Image Compression enhances the progress of the world in communication [4] [7] [11].…”

Section: Image Compressionmentioning

confidence: 99%

Neural Network Technique for Lossless ImageCompression Using X-Ray Images

Senthilkumaran¹,

Suguna²

2011

IJCEE

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“…A detail survey of about how ANN can be applied for compression purpose is reported in [1,14,15,16,17]. Broadly, two different categories for improving the compression methods and performance have been suggested.…”

Section: Introductionmentioning

confidence: 99%

Modeling of neural image compression using GA and BP: a comparative approach

Rajput¹,

Shivashetty²,

Singh³

2011

SpecialIssue

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Abstract-It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate; the decompressed image gets really fuzzy or indistinguishable. To overcome problems associated with conventional methods, artificial neural networks based method can be used. Genetic algorithm is a very powerful method for solving real life problems and this has been proven by applying to number of different applications. There is lots of interest to involve the GA with ANN for various reasons at various levels. Trapping in the local minima is one of the well-known problems of gradient decent based learning in ANN. The problem can be addressed using GA algorithm. But no work has been done to evaluate the performance of both learning methods from the image compression point of view. In this paper, we investigate the performance of ANN with GA in the application of image compression for obtaining optimal set of weights. Direct method of compression has been applied with neural network to get the additive advantage for security of compressed data. The experiments reveal that the standard BP with proper parameters provide good generalize capability for compression and is much faster compared to earlier work in the literature, based on cumulative distribution function. Further, the results obtained shows that general concept about GA, it performs better over gradient decent based learning, is not applicable for image compression.

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Artificial neural network for image compression

Cited by 49 publications

References 1 publication

Neural Network Technique for Lossless Image Compression Using X-Ray Images

Neural Network Technique for Lossless Image Compression Using X-Ray Images

Neural Network Technique for Lossless ImageCompression Using X-Ray Images

Modeling of neural image compression using GA and BP: a comparative approach

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