Comparative Study of Vector Quantization in Image Compression

  • Authors

    • Karthikeyan N
    • Sivakumar M
    • Nandini Devi R
    • Pavithra M
    • Dr Saravanakumar N M
    https://doi.org/10.14419/ijet.v7i3.34.19584
  • Compression, Transformation - Discrete Cosine Transformation (DCT), Discrete Wavelet Transformation (DWT), Quantization-LBG algorithm.

  • Abstract

    Image Compression holds a technique of compressing the data which encodes the standard image with fewer amount of bits, which results from particular sampling values using quantization methods. Quantization is a lossy compression technique, which is done by minimizing a set of bits into single quantum bit. In Vector Quantization, codebook is the significant part. The various techniques for quantization are explained in this paper. Some popular quantization techniques consume less memory and computation time such as LBG80, FBP, BPNN, ENN and Simple nearest neighbor algorithm. The resultant values of different quantization are explained in an experimental part, with the use of standard performance measure of MSE and PSNR. This approach gives an effective Codebook with minimum computational time and includes excellent Peak Signal to Noise Ratio values (PSNR).

     

     

  • References

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  • How to Cite

    N, K., M, S., Devi R, N., M, P., & Saravanakumar N M, D. (2018). Comparative Study of Vector Quantization in Image Compression. International Journal of Engineering & Technology, 7(3.34), 895-899. https://doi.org/10.14419/ijet.v7i3.34.19584

    Received date: 2018-09-12

    Accepted date: 2018-09-12