IMAGE COMPRESSION USING TRANSFORM TECHNIQUES

Because oftlie energy compaction property of transform coding, it ispossible to code only a fraction of the transform coefficients without seriously affecting the image. This allows us to code images at bit rates below 1 bit/pixel with a relatively small sacrifice in image quality and intelligibility.• OCT discrete cosme transform is one of the most techniques used in international image compression standard such as JPEG & MPEG. It is found that it give good results up to 32: 1 CR, after that it suffer greatly from blocking artifacts.• Applying the Haar wavelet method it gives results close to OCT at high compression ratio or (low-bit rate coding) about 64: 1 CR. But it is found that Haar wavelet require less computation than OCT.• It is noted that 9-7 daubechies wavelet with embedded zero wavelet coding give the best results.While OCT -based image coders perform very well at moderate bit rates, at higher compression ratios, image quality degrades because of the artifacts resulting from the block-based OCT scheme. Waveletbased coding on the otherhand provides substantial improvement in picture’ quality at low bit rates due to overlapping basis functions and better energy compaction property of wavelet transforms. Because of the inherent multiresolution nature, wavelet-based coders facilitate progressIve transmission of images thereby allowing achieving variable bit rates.• Both the encoding technique and the particular wavelet used can make a significant difference in the perfonnance of a compression system: the zerotree coder performs the best.• The importance of searching and using good wavelet filters in most coding schemes can’t be over emphasized. They are currently work ing on algorithms to dynamically determine the right wavelet filter based on the type and statistical nature of the input image to be coded~• Wavelet techniques based on multiresolution analysis are amongthe most promising techniques available today.• A modified method for blocking artifact reduction is introduced.The proposed method uses the wavelet decomposition technique to separate blocking noise from the image data. The proposed technique is designed to work in the sub-band domain. Once a reconstructed image is decomposed into sub-bands by wavelet filters, most energy of the blocky nOIse exists on the predetermined block boundaries of their corresponding sub-bands, we can reduce the blocky noise by average filters, median ,filters or LMMSE filter in each sub-band.The result presented illustrates that the method is effective in reducing this difficult type of noise in the reconstructed images. This technique is a post processing technique which allows the use of very low bit rate and further reduction in compression ratios. This helps in fast transmission and small size registration of images.• Using Wavelet Transform in video coding.• Using Wavelet Transform in reducing problems appearing with video compreSSIOn.• Dynamic determination of the right wavelet filter based on the type and statistical nature of the input image.