On neural network design ? Part I: Using the MVQ algorithm

Porter, William A.; AbouAli, Abdellatief Hussien

doi:10.1007/bf01202853

Cited by 5 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results reflect that the image features remain stable in the high compression ratio. The proposed algorithm also implemented the codebook bins for better improvement in the quality of the area [77][78]. Guo et al ( 2014) proposed the VQ method for the image segmentation.…”

Section: The Huffman Codingmentioning

confidence: 99%

Image Compression Using Different Optimization Algorithms: A Review

2021

Aswan University Journal of Sciences and Technology

View full text Add to dashboard Cite

Image compression is most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression technique involves reducing the size of the image without degrading the quality of the image. Currently many image compression algorithms are used to deal with increasing amount of data involved but still finding the alternative solution is the area of research as shown in Fig 1.Fig 1 Image Compression

show abstract

Section: The Huffman Codingmentioning

confidence: 99%

Image Compression Using Different Optimization Algorithms: A Review

2021

Aswan University Journal of Sciences and Technology

View full text Add to dashboard Cite

show abstract

“…The vector quantization puts the input samples into groups of well-defined vectors based on the distortion measure. The vector quantization has been widely used, beside the encoding/compression, in applications such as pattern recognition, speech recognition, face detection and neural networks design [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Object-based VQ for image compression

Abouali

2015

Ain Shams Engineering Journal

View full text Add to dashboard Cite

In this paper, a vector quantization algorithm is proposed for image compression. The proposed algorithm composed of three phases, initialization, iterative and finalization. The initialization phase based on Max-Min algorithm. The iterative phase is an adaptive LBG algorithm. The finalization frees the codebook from redundancy. The LBG adapted to locate the codebook points to line up across the boundary of the objects they represent. The algorithm testing results showed that images main features sustain the high compression ratios. The same algorithm is applicable to codebook bins to improve the quality for the areas that have fine details. Ó 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

show abstract

Function emulation using MVQ neural networks

Porter

Abouali

1999

Circuits Systems and Signal Process

View full text Add to dashboard Cite

On neural network design ? Part I: Using the MVQ algorithm

Cited by 5 publications

References 11 publications

Image Compression Using Different Optimization Algorithms: A Review

Image Compression Using Different Optimization Algorithms: A Review

Object-based VQ for image compression

Function emulation using MVQ neural networks

Contact Info

Product

Resources

About