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Side match and overlap match vector quantizers for images.

T Kim1

  • 1AT&T Bell Lab., Murray Hill, NJ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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Finite state vector quantizers (FSVQs) with memory improve image coding. Side match vector quantizers (SMVQs) and overlap match vector quantizers (OMVQs) reduce noise and bit rates, enhancing perceived image quality.

Area of Science:

  • Digital Image Processing
  • Data Compression
  • Signal Processing

Background:

  • Traditional Vector Quantization (VQ) in image coding suffers from granular noise, leading to visible pixel block boundaries.
  • High spatial correlation in gray-level images is often underutilized in standard VQ methods.
  • Minimizing noise and improving perceived quality are key challenges in efficient image compression.

Purpose of the Study:

  • To investigate a class of vector quantizers with memory, known as finite state vector quantizers (FSVQs), for image coding.
  • To introduce and evaluate two novel FSVQ designs: side match vector quantizers (SMVQs) and overlap match vector quantizers (OMVQs).
  • To leverage 2-D spatial contiguity and pixel correlation for enhanced image compression performance.

Main Methods:

Related Experiment Videos

  • Development of Side Match Vector Quantizers (SMVQs) and Overlap Match Vector Quantizers (OMVQs).
  • Exploitation of 2-D spatial contiguity and high pixel correlation in gray-level images.
  • Application of these methods to 512x512 gray-level images for quality assessment.

Main Results:

  • SMVQs and OMVQs achieve communication quality reproduction at an average of 0.5 bits/pixel.
  • These methods significantly reduce the visibility of granular noise and block boundaries compared to ordinary VQ.
  • Variable length noiseless codes, combined with SMVQ/OMVQ, yield up to a 60% bit rate reduction over fixed-length codes.

Conclusions:

  • FSVQs, specifically SMVQ and OMVQ, offer substantial improvements in image coding efficiency and quality.
  • The proposed designs effectively minimize noise, leading to greater perceived quality enhancement.
  • Significant bit rate reductions are achievable, making these methods highly promising for image compression applications.