Fuzzy Vector Quantization of Images Based on Local Fractal Dimensions

Sasazaki, K.; Ogasawara, Hiroshi; Saga, Sato; Maeda, Junji; Suzuki, Yusuke

doi:10.1109/fuzzy.2006.1681843

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The output of the hidden layer of neural network is quantized to achieve further compression. Various vector quantization [36] algorithms like k-means, FVQ1, FVQ2 andFVQ3 were used for code book design in different sets of experiments. Finally, these quantized values are entropy encoded using Huffman encoding.…”

Section: Quantizationmentioning

confidence: 99%

Biomedical Image Compression Techniques for Clinical Image Processing

Saudagar

2020

Int. J. Onl. Eng.

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Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.

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Section: Quantizationmentioning

confidence: 99%

Biomedical Image Compression Techniques for Clinical Image Processing

Saudagar

2020

Int. J. Onl. Eng.

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“…17 Sasazaki et al suggested to use local fractal dimension to design a codebook and used fuzzy k-means algorithm to group the training vectors into clusters. 18 This paper presents an efficient algorithm for image compression, which exploits the advantages offered by wavelet transform, neural network and fuzzy clustering algorithm. The proposed algorithm encodes the images at low bit rate with good visual quality.…”

Section: Related Literaturementioning

confidence: 99%

A Neuro-Wavelet Model Using Fuzzy Vector Quantization for Efficient Image Compression

Singh

Rajpal

Murthy

2009

Int. J. Image Grap.

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Images have large data quantity. For storage and transmission of images, high efficiency image compression methods are under wide attention. In this paper, we propose a neurowavelet based model for image compression, which combines the advantages of wavelet transform and neural network and uses fuzzy vector quantization on hidden layer coefficients. Images are decomposed using wavelet filters into a set of sub bands with different resolution corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. The coefficients in the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients in higher frequency bands are compressed using neural network. The coefficients of the hidden layer of the neural network are further fuzzy vector quantized, which increases the compression ratio. The visual quality of the image has been increased by introducing fuzziness to vector quantization algorithm. Satisfactory reconstructed images with large compression ratios have been achieved using this scheme.

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“…Bezdek (1984) extended Dunn's formulation and produced a family of fuzzy K-means algorithms, which includes Dunn's original algorithm as a special case. Sasazaki et al (2006) suggested the use of local fractal dimension to design a codebook and used fuzzy K-means algorithm to group the training vectors into clusters.…”

Section: Introductionmentioning

confidence: 99%

Design of a neuro fuzzy model for image compression in wavelet domain

Singh

Rajpal

Murthy

2009

J Indian Soc Remote Sens

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Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in the data present within an image. This work evaluates the performance of an image compression system based on fuzzy vector quantization, wavelet-based sub band decomposition and neural network. The vector quantization is often used when high compression ratios are required. The implementation consists of three steps: first, the image is decomposed into a set of sub bands with different resolutions corresponding to different frequency bands. Different quantization and coding schemes are used for different sub bands based on their statistical properties. In the second step, wavelet coefficients corresponding to the lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. Finally, the result of the second step was used as input to fuzzy vector quantizer. Image quality was compared objectively using mean squared error and peak signal to noise ratio along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality when compared with other image compression techniques (Liu, 2005;Premaraju, 1996).

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Fuzzy Vector Quantization of Images Based on Local Fractal Dimensions

Cited by 5 publications

References 9 publications

Biomedical Image Compression Techniques for Clinical Image Processing

Biomedical Image Compression Techniques for Clinical Image Processing

A Neuro-Wavelet Model Using Fuzzy Vector Quantization for Efficient Image Compression

Design of a neuro fuzzy model for image compression in wavelet domain

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