Encoding of Still Pictures by Wavelet Transform with Vector Quantization Using a Rough Fuzzy Neural Network(Image Processing, Image Pattern Recognition)

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概要

• 論文の詳細を見る

• In this paper color image compression using a fuzzy Hopfield-model net based on rough-set reasoning is created to generate optimal codebook based on Vector Quantization (VQ) in Discrete Wavelet Transform (DWT). The main purpose is to embed rough-set learning scheme into the fuzzy Hopfield network to construct a compression system named Rough Fuzzy Hopfield Net (RFHN). First a color image is decomposed into 3-D pyramid structure with various frequency bands. Then the RFHN is used to create different codebooks for various bands. The energy function of RFHN is defined as the upper-and lower-bound fuzzy membership grades between training samples and codevectors. Finally, near global-minimum codebooks in frequency domain can be obtained when the energy function converges to a stable state. Therefore, only 3 × 2 / N pixels are selected as the training samples if a 3 × N-dimensional color image was used. In the simulation results, the proposed network not only reduces the consuming time but also preserves the compression performance.

• 社団法人電子情報通信学会の論文
• 2003-09-01

著者

• Liu Shao-han

  Department Of Electronic Engineering National Chin-yi Institute Of Technology

• Lin J‐s

  National Chin‐yi Inst. Technol. Taichung Twn

• Lin Jzau-sheng

  Department Of Electronic Engineering National Chin-yi Institute Of Technology

関連論文

• The Application of Fuzzy Hopfield Neural Network to Design Better Codebook for Image Vector Quantization(Special Section on Digital Signal Processing)
• Encoding of Still Pictures by Wavelet Transform with Vector Quantization Using a Rough Fuzzy Neural Network(Image Processing, Image Pattern Recognition)

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