Blur identification from vector quantizer encoder distortion

K. Panchapakesan, D. G. Sheppard, M. W. Marcellin, B. R. Hunt

Research output: Contribution to journalLetterpeer-review

22 Scopus citations


Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB.

Original languageEnglish (US)
Pages (from-to)465-470
Number of pages6
JournalIEEE Transactions on Image Processing
Issue number3
StatePublished - Mar 2001


  • Blur identification
  • Compression
  • Signal processing
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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