929 words - 4 pages

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 ...view middle of the document...

Size of the vector is variable

and depends on the level of decomposition. It is smaller at

higher level of decomposition. In 141 vector is formed by

cross

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Image and Video Coding 1905

Methodl

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

PSNR I C

' . PRIORITY BASED ENCODING

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 method gives good results even for full search

non-constrained VQ,

Variance criterion cannot be used for interband system

because coefficient values show large variations and hence

variance cannot truly identify the true important vectors.

RESULTS

. Results are tabulated in table1

Image was decomposed up to 3 levels using 'Haar wavelet'.

Most coarse version of the image (LL3) IS scalar quantized.

&!!mil Results are...

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