Combined compression and denoising of images using vector quantization

Kannan Panchapakesan, Ali Bilgin, David G. Sheppard, Michael W. Marcellin, Bobby R. Hunt

Research output: Contribution to journalConference article

Abstract

Compression of a noisy source is usually a two stage problem, involving the operations of estimation (denoising) and quantization. A survey of literature on this problem reveals that for the squared error distortion measure, the best possible compression strategy is to subject the noisy source to an optimal estimator followed by an optimal quantizer for the estimate. What we present in this paper is a simple but sub-optimal vector quantization (VQ) strategy that combines estimation and compression in one efficient step. The idea is to train a VQ on pairs of noisy and clean images. When presented with a noisy image, our VQ-based system estimates the noise variance and then performs joint denoising and compression. Simulations performed on images corrupted by additive, white, Gaussian noise (AWGN) show significant denoising at various bit rates. Results also indicate that our system is robust enough to handle a wide range of noise variances, while designed for a particular noise variance.

Original languageEnglish (US)
Pages (from-to)523-529
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3460
DOIs
StatePublished - Dec 1 1998
EventApplications of Digital Image Processing XXI - San Diego, CA, United States
Duration: Jul 21 1998Jul 24 1998

Keywords

  • Compression
  • Denoising
  • Estimation
  • Noisy Source Coding
  • Non-linear Interpolative Vector Quantization
  • Vector Quantization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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