Image compression with a hierarchical neural network

Namphol, A.; Chin, Steven H.; Arozullah, M.

doi:10.1109/7.481272

Cited by 79 publications

(40 citation statements)

References 8 publications

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“…The number of neurons at the middle layer (hidden layer) is lesser than number of neurons at the input layer, to achieve compression. The compression rate can be increase by adding more number of hidden layers [5].…”

Section: Artificial Neural Network For Image Compressionmentioning

confidence: 99%

Optimized Image Compression through Artificial Neural Networks and Wavelet Theory

Singh¹,

Alam²,

Saini³

2013

IJCA

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Many techniques have been developed for image compression. An efficient image compression technique promises to give high compression ratio, maintaining the quality of the image. The paper proposes an image compression technique which combines both Artificial Neural Networks and Wavelet theory to optimize the compression ratio and peak signal to noise ratio. Results show that high compression ratio is achievable as per requirement, maintaining good reconstruction quality. General TermsImage Compression

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Section: Artificial Neural Network For Image Compressionmentioning

confidence: 99%

Optimized Image Compression through Artificial Neural Networks and Wavelet Theory

Singh¹,

Alam²,

Saini³

2013

IJCA

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“…It has become increasingly important to most computer networks, as the volume of data traffic has begun to exceed their capacity for transmission. Traditional techniques that have already been identified for data compression include: Predictive Coding, Transform coding and Vector Quantization [1,2]. In brief, predictive coding refers to the decor relation of similar neighboring pixels within an image to remove redundancy.…”

Section: Introductionmentioning

confidence: 99%

“…The results achieved with a transform based technique is highly dependent on the choice of transformation used (cosine, wavelet, Karhunen-Loeve etc.) [2]. Finally vector quantization techniques require the development of an appropriate codebook to compress data.…”

Section: Introductionmentioning

confidence: 99%

ASIC Implementation of Neural Network Based Image Compression

Ramanaiah¹,

Raj²

2011

IJCTE

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Abstract-Image data consumes enormous bandwidth and storage space. Neural networks can be used for image compression. Neural network architectures have proven to be more reliable, robust, and programmable and offer better performance when compared with classical techniques. In this paper the main focus is development of new architectures for neural network based image compression optimizing area, power and speed as specific to ASIC implementation, and comparison with FPGA.The proposed architecture designs are realized on Spartan IIIE FPGA Using Xilinx ISE, and the ASIC implementation is carried out using Synopsys tools targeting 130nm TSMC Library. The ASIC implementation for 2 input and 16 input neuron with low power techniques adopted such as buffer insertion, clock gating etc, Index Terms-Image compression, neural networks, FPGA, ASIC, CSD

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“…Image compression is a context where images of different types and different sizes are compressed using different methodologies while data compression is the only solution to meet large storage space requirements and ever growing bandwidth requirements [1].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Image Coding Scheme Combining Wavelets, Neural Networks and Differential Pulse Code Modulation for Effectual Image Compression

S¹,

P²,

Ramanaiah³

2013

IJCA

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Large images consume more storage space needing high data rates for transmission demanding the innovation of efficient image compression systems. Owing to the massive parallel architecture and generalization ability of neural networks to memorize inputs even on untrained data, the computational simplicity of wavelets, ability of Differential Pulse Code Modulation (DPCM) to reduce the unused or redundant bits in the information, in this paper an hybrid image compression system combining the advantages of wavelets and neural networks is implemented along with Differential Pulse Code Modulation based on the predicted sample values. Scalar quantization and Huffman encoding schemes are used as well for compressing different sub bands i.e the low frequency band coefficients are compressed by the DPCM while the high frequency band coefficients are compressed using neural networks. Satisfactory reconstructed images with increased bit rates and large Peak Signal to Noise Ratio (PSNR) can be achieved with this scheme. Wavelet transform eliminates the blocking artefacts' associated with cosine transform and neural networks minimize the Mean Square Error (MSE).Empirical analysis and metrics calculation is performed for the sake of relative analysis.

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Image compression with a hierarchical neural network

Cited by 79 publications

References 8 publications

Optimized Image Compression through Artificial Neural Networks and Wavelet Theory

Optimized Image Compression through Artificial Neural Networks and Wavelet Theory

ASIC Implementation of Neural Network Based Image Compression

An Efficient Hybrid Image Coding Scheme Combining Wavelets, Neural Networks and Differential Pulse Code Modulation for Effectual Image Compression

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