Comparing Vector Quantization and Wavelet Coefficients Essays

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Wavelet transform is efficient tool for image compression,
Wavelet transform gives multiresolution image decomposition. which can .be exploited through vector quantization to achieve high compression ratio. For vector quantization of wavelet coefficients vectors are formed by either coefficients at same level, different location or different level, same location.
This paper compares the two methods and shows that because of wavelet properties, vector quantization can still improve compression results by coding only important vectors for reconstruction. Thus giving priority to the important vectors higher compression can be achieved at better quality. The algorithm is also useful for embedded vector quantization coding of wavelet
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Image and Video Coding 1905
PSNR I C band technique. Cross band technique takes the advantages of nterband dependency and improves compression.
If we take into consideration HVS response, all the coefficients are not important for image representation. This visual redundancy can be removed to improve the compression ratio further [5]. Edges in the image are more imponant for good quality image reconstruction. Vectors giving edge information are more important .Giving priority to such important vectors embedded coding can be achieved.
Method 2
Wavelet decomposition represents edges in horizontal vertical and diagonal direction. If we code only the coefficients representing edges, image reconstruction at reduced rate is possible. To find edge region, variance of the adjacent coefficients can be considered. In vector quantization if the vectors are formed with adjacent coefficients from the same band at same
Location, variance of the vectors represent edge region.
Quality of the reconstructed image by coding only high variance vectors is much better than interband vector quantiation. Codebook is generated including high variance vectors from training images. This results into close match for important vectors and improves quality [6].
This simple
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