A Neuro Fuzzy Model for Image Compression in Wavelet Domain

  • Vipula Singh
  • Navin Rajpal
  • K. Srikanta Murthy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)


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 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. Vector quantization is often used when high compression ratios are required. The implementation consists of three steps. First, image is decomposed 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. At the second step, the wavelet coefficients corresponding to lowest frequency band are compressed by differential pulse code modulation (DPCM) and the coefficients corresponding to higher frequency bands are compressed using neural network. The result of the second step is used as input to fuzzy vector quantizer. Image quality is compared objectively using mean squared error and PSNR along with the visual appearance. The simulation results show clear performance improvement with respect to decoded picture quality as compared to other image compression techniques.


Mean Square Error Wavelet Coefficient Image Compression Vector Quantization Image Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vipula Singh
    • 1
  • Navin Rajpal
    • 2
  • K. Srikanta Murthy
    • 3
  1. 1.Research Scholar GGSIPU, New Delhi, Assistant Professor ECE dept PESIT Bangalore 
  2. 2.Professor GGSIPUNew Delhi
  3. 3.Professor IS Dept PESIT Bangalore 

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