Efficient color image compression using integrated fuzzy neural networks for vector quantization

Sunanda Mitra, Surya Pemmaraju, Sastry Kompella, Steven Meadows

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


An efficient design of a vector quantizer for image compression is dependent on optimization criteria used in partitioning similar vector groups formed from the image in the spatial or transform domain. Usually a 3-D vector quantization is applied to trivariant color spaces. Current trends in image coding exploit the advantage of subband/wavelet decompositions in reducing the complexity in optimal scalar/vector quantizer (SQ/VQ) design. We have used adaptive vector quantization of wavelet coefficients of subimages in each color plane in RGB color space. Our design of vector quantizers using two neuro-fuzzy clustering algorithms namely AFLC-VQ, and IAFC-VQ generates very low bit rate image encoder in color as well as monochrome and outperforms other similar designs in minimizing distortion arising from quantization. Further tuning of these algorithms, and addition of an entropy coder module after the VQ stage could result in extremely low bit rates (compression ratio around 100:1) at minimal distortion.

Original languageEnglish
Pages (from-to)184-188
Number of pages5
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
StatePublished - 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA
Duration: Oct 12 1997Oct 15 1997


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