Progressive low bit rate digital color/monochrome image coding by neuro-fuzzy clustering

Sunanda Mitra, Steven Meadows

Research output: Contribution to journalConference articlepeer-review

Abstract

Color image coding at low bit rates is an area of research that is just being addressed in recent literature since the problems of storage and transmission of color images are becoming more prominent in many applications. Current trends in image coding exploit the advantage of subband/wavelet decompositions in reducing the complexity in optimal scalar/vector quantizer (SQ/VQ) design. Compression ratios (CRs) of the order of 10:1 to 20:1 with high visual quality have been achieved by using vector quantization of subband decomposed color images in perceptually weighted color spaces. We report the performance of a recently developed adaptive vector quantizer, namely, AFLC-VQ for effective reduction in bit rates while maintaining high visual quality of reconstructed color as well as monochrome images. For 24 bit color images, excellent visual quality is maintained upto a bit rate reduction to approximately 0.48 bpp (for each color plane or monochrome 0.16 bpp, CR 50:1) by using the RGB color space. Further tuning of the AFLC-VQ, and addition of an entropy coder module after the VQ stage results in extremely low bit rates (CR 80:1) for good quality, reconstructed images. Our recent study also reveals that for similar visual quality, RGB color space requires less bits/pixel than either the YIQ, or HIS color space for storing the same information when entropy coding is applied. AFLC-VQ outperforms other standard VQ and adaptive SQ techniques in retaining visual fidelity at similar bit rate reduction.

Original languageEnglish
Pages (from-to)170-176
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3165
DOIs
StatePublished - 1997
EventApplications of Soft Computing - San Diego, CA, United States
Duration: Jul 28 1997Jul 28 1997

Keywords

  • Adaptive VQ
  • Color spaces
  • Entropy coding
  • Low bit rate coding
  • Neuro-fuzzy clustering
  • Visual fidelity

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