Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/11491
Title: ULTRASONIC IMAGE COMPRESSION USING SU$BAND HIERARCHICAL BLOCK PARTITIONING
Authors: Vasamseti, Srikanth
Keywords: ELECTRICAL ENGINEERINGe;ELECTRICAL ENGINEERING;ELECTRICAL ENGINEERING;ELECTRICAL ENGINEERING
Issue Date: 2010
Abstract: Image compression is fundamental to the efficient and cost-effective use of medical imaging technology and applications such as teleradiology and image archiving. The transmission time reduces drastically due to compression of image. The jpeg, jpeg2000 and SBHP are the well known compression methods. In the jpeg algorithm, the image has been divided into blocks. The DCT is to be applied on each 8 by 8 block of image to retrieve the coefficients. To represent compressed data, coefficients of each block have been captured in zigzag arder. The reconstructed image from compressed image is to be obtained by applying the reverse process of encoding. By using jpeg algorithm blocking artifacts are there in the reconstructed image. Due to these artifacts reconstructed images seems to be blurred images. To overcome this problem jpeg2000 algorithm has been implemented. In the jpeg2000 algorithm, discrete wavelet transform is applied to decompose the image into sub-bands at different resolutions. We perform the precincts, scan patter, and compress bit stream operations to represent the compressed data. Reverse process of encoding has been applied on compressed data to obtain the reconstructed image. In this algorithm energy concentration doesn't consider in the same level. In the SBHP, the energy concentrations were available in the both frequency and space. In the SBHP algorithm, discrete wavelet transform applied to decompose the image into sub-bands at different resolutions. First, the root sub-band has been chosen for checking the significant. The sub-band is significant if any one of the pixel value in the sub-band greater than threshold value. Significant sub-band partitioned into four parts that's called Quadtree partitioning and again checks for significant for each partitioned part. This partitioned process continues on each part until size comes to 1, i.e. one pixel that is to be used for encoding. There are two types of pixels, significant pixel and non significant pixel which is depends on significant condition. A pixel is significant, if the pixel value greater than threshold value otherwise not. The significant pixel encodes as 1 otherwise 0 with sign of pixel 1 for positive and 0 for negative. If root sub-band or any sub-band is not a significant sub band then it included in List of Insignificant Set.
URI: http://hdl.handle.net/123456789/11491
Other Identifiers: M.Tech
Research Supervisor/ Guide: Kumar, Vinod
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' DISSERTATIONS (Electrical Engg)

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