Efficient image coding using multiresolution wavelet transform and vector quantization

Surya Pemmaraju, Sunanda Mitra

Research output: Contribution to conferencePaper

8 Scopus citations

Abstract

Image compression forms the backbone for several applications such as storage of images in a database, picture archiving, TV and facsimile transmission, and video conferencing. Compression of images involves taking advantage of the redundancy in data present within an image. A fundamental goal of image compression is to reduce the bit rate for transmission and storage while maintaining an acceptable fidelity or image quality. Existing VQ algorithms however, suffer from a number of practical problems e.g. codebook initialization, long search process, and getting trapped in local minima. This paper presents an adaptive vector quantization algorithm which uses a neuro-fuzzy clustering technique for optimizing the distortion measure. The fuzzy approach forms the basis for accurately optimizing each codevector by determining the fuzzy centroid of each class. In addition, a multiresolution wavelet decomposition scheme is adopted to make the image better suited for compression and to enable its progressive transmission.

Original languageEnglish
Pages135-140
Number of pages6
StatePublished - 1996
EventProceedings of the 1996 IEEE Southwest Symposium on Image Analysis and Interpretation - San Antonio, TX, USA
Duration: Apr 8 1996Apr 9 1996

Conference

ConferenceProceedings of the 1996 IEEE Southwest Symposium on Image Analysis and Interpretation
CitySan Antonio, TX, USA
Period04/8/9604/9/96

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