Medical image compression and vector quantization

Medical Image Compression Using Vector Quantization and Gaussian Mixture Model

Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a med...

Vector Quantization for Image Compression-Concept Quantization

Vector Quantization Based Image Compression

An image compression method combining discrete wavelet transform (DWT) and vector quantization (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate sub bands. These sub bands are then vector quantized. VQ indices are Huffman coded to increase the compression ratio. Lloyd extended scalar quantization technique is used to design memory less vect...

Medical Image Compression Using Region-of-interest Vector Quantization

In this paper an image compression approach is proposed for medical applications. The algorithm is based on the Vector Quantization and adopts the idea of Region-OfInterest. The image to be compressed is first segmented into regions and a separate codebook is used for compressing every region. The size and the number of codewords may be different in the codebooks according to the diagnostic imp...

Image Compression Using Hybrid Vector Quantization

In this paper, image compression using hybrid vector quantization scheme such as Multistage Vector Quantization (MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A combined MSVQ and PVQ are utilized to take advantages provided by both of them. In the wavelet decomposition of the image, most of the information often resides in the lowest frequency subband. MSVQ is applied to significa...