DESIGN OF NEURO-WAVELET BASED VECTOR QUANTIZER FOR IMAGE COMPRESSION

This paper presents a novel approach to design a vector quantizer for image compression. Compression of image data using Vector Quantization (VQ) will compare Training Vectors with Codebook that has been designed. The result is an index of position with minimum distortion. Moreover it provides a means of decomposition of the signal in an approach which takes the improvement of inter and intra band correlation as more lithe partition for higher dimension vector spaces. Thus, the image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. In addition the proposed paper also presents a survey on different methods of vector quantization for image compression and application of SOFM

An Intelligent Method Based Medical Image Compression

Compression methods are important in many medical applications to ensure fast interactivity through large sets of images (e.g. volumetric data sets, image databases), for searching context dependant images and for quantitative analysis of measured data. Medical data are increasingly represented in digital form. The limitations in transmission bandwidth and storage space on one side and the growing size of image datasets on the other side has necessitated the need for efficient methods and tools for implementation. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for Teleradiology. This paper presents an approach of intelligent method to design a vector quantizer for image compression. The image is compressed without any loss of information. It also provides a comparative study in the view of simplicity, storage space, robustness and transfer time of various vector quantization methods. The proposed approach presents an efficient method of vector quantization for image compression and application of SOFM

Vector Quantization Video Encoder Using Hierarchical Cache Memory Scheme

A system compresses image blocks via successive hierarchical stages and motion encoders which employ caches updated by stack replacement algorithms. Initially, a background detector compares the present image block with a corresponding previously encoded image block and if similar, the background detector terminates the encoding procedure by setting a flag bit. Otherwise, the image block is decomposed into smaller present image subblocks. The smaller present image subblocks are each compared with a corresponding previously encoded image subblock of comparable size within the present image block. When a present image subblock is similar to a corresponding previously encoded image subblock, then the procedure is terminated by setting a flag bit. Alternatively, the present image subblock is forwarded to a motion encoder where it is compared with displaced image subblocks, which are formed by displacing previously encoded image subblocks by motion vectors that are stored in a cache, to derive a first distortion vector. When the first distortion vector is below a first threshold TM, the procedure is terminated and the present image subblock is encoded by setting flag bit and a cache index corresponding to the first distortion vector. Alternatively, the present image subblock is passed to a block matching encoder where it is compared with other previously encoded image subblocks to derive a second distortion vector. When the second distortion vector is below a second threshold Tm, the procedure is terminated by setting a flag bit, by generating the second distortion vector, and by updating the cache.Georgia Tech Research Corporatio