Wavelet image compression using universal coefficients matrix detail estimation

Sánchez, Lorenzo; Meana, Hector Manuel Perez; Miyatake, Mariko Nakano

doi:10.1109/icecc.2004.1269586

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…of iterations. While comparing the proposed method with the existing works done by Sanchez [24], Sudhakar [25] and Joshi [26], our proposed method gives better performance.…”

Section: Resultssupporting

confidence: 48%

An effective way of image compression using DWT and SOM based vector quantisation

Kalaivani¹,

Thirumaraiselvi²,

Sudhakar³

2013

2013 IEEE International Conference on Computational Intelligence and Computing Research

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Vector quantisation is a novel and attractive technique for still image compression. The most significant advantages are high reconstruction quality at low coding rates, rapid decoding. One of the major disadvantages is high encoding time complexity. Different algorithms are being developed to reduce the search space, so that encoding time complexity can be reduced to a large extent. An algorithm based on Kohonen's self organizing maps is proposed in this paper for quantising the image data. This algorithm uses its roots in neural networks based on neighbourhood relationships. Wavelet transform is also a cutting edge technology in the field of image compression. This provides substantial improvement in picture quality at high compression ratios. Experimental results obtained demonstrate the effectiveness of the proposed algorithm.keywords-Neural network, SOM, DWT, image compression

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“…of iterations. While comparing the proposed method with the existing works done by Sanchez [24], Sudhakar [25] and Joshi [26], our proposed method gives better performance.…”

Section: Resultssupporting

confidence: 48%

An effective way of image compression using DWT and SOM based vector quantisation

Kalaivani¹,

Thirumaraiselvi²,

Sudhakar³

2013

2013 IEEE International Conference on Computational Intelligence and Computing Research

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“…The reason why the approximate subband is keep unchanged is because the modification in approximate subband will destroy a large amount of significant coefficients that may lead to lossy compression [11]. The details subband that consist of a large amount of near zerocoefficients can be ignored because it will not harming the image quality.…”

Section: Proposed Methodologymentioning

confidence: 99%

Image compression using a new adaptive standard deviation thresholding estimation at the wavelet details subbands

Taujuddin

Ibrahim

Sari

2015

2015 Second International Conference on Computing Technology and Information Management (ICCTIM)

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Abstract-The process before quantization stage in compression process is a very crucial stage espeacially in application that require a high compression ratios. So, in this paper, we propose a new method of image compression that is based on reducing the wavelet coefficients in wavelet details subbands. It is based on the concept of local subband wavelet coefficients minimization to find the optimum threshold value for wavelet coefficients in each detail subbands. The proposed method decomposed the image into LL (low resolution approximate image), HL (intensity variation along column, horizontal edge), LH (intensity variation along row, vertical edge) and HH (intensity variation along diagonal). The coefficients in details subband retrived from the decomposition process is then manipulated in such a way that the nearly zero coefficient is discarded while the rest is remained. This process will reduce the unsignificant wavelet coefficient that leads to a great compression ratio while preserving the informative data to produce a good image quality as can be seen in the experiment done.

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Self organizing map and wavelet based image compression

Kathirvalavakumar¹,

Ponmalar²

2012

Int. J. Mach. Learn. & Cyber.

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Wavelet image compression using universal coefficients matrix detail estimation

Cited by 4 publications

References 4 publications

An effective way of image compression using DWT and SOM based vector quantisation

An effective way of image compression using DWT and SOM based vector quantisation

Image compression using a new adaptive standard deviation thresholding estimation at the wavelet details subbands

Self organizing map and wavelet based image compression

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