As Image size need to be reduced for the purpose of data storage and transmission for better utilization of the Bandwidth, Images are compressed using lossy and Lossless compression schemes. In this paper Fractal Image Compression, a lossy compression technique is being implemented on medical Images .Fractal Encoding involves partitioning the images into Range Blocks and Domain Blocks and each Range Block is mapped onto the Domain Blocks by using contractive transforms called the Affine Transforms. The Fractal encoding technique takes a longer encoding time and less decoding time. In the present paper Fractal Image Compression using quad-tree Partitioning technique is being implemented. on Medical Images like CT Of Bone and MR Images of Brain The Performance measures like Compression Ratio (CR), Peak Signal To Noise Ratio (PSNR), Mean Square Error (MSE), Encoding time and Decoding Time are determined for the Range Blocks of Sizes 2x2 and 4x4 respectively with different Threshold Values. Mat lab simulated results for these Performance Measures shows that for larger Range Block size, the PSNR Value decreases and CR increases for different threshold Values.
This paper examines the properties of Number Theoretic Transforms over FFT. The aim of this study is to show that Number Theoretic Transforms (NTTs) can be really beneficial in terms of error free and faster computation. One and two dimensional NTTs are implemented in MATLAB and properties are verified and as an example convolution is implemented using Fermat number transform(FNT) which is a variant of NTT.A study of comparison of NTT to FFT proves that NTTs are really beneficial in terms of computational complexity and error free computation.
Images are compressed using lossy and lossless compression schemes to utilize the Bandwidth in an effective way and also to provide enough space for storing voluminous data. Fractal Image Compression (FIC), is one such lossy compression scheme based on contractive mapping theorem. Affine transforms are being employed in Fractal Image compression to map the range blocks and domain blocks incorporating the property of self similarity in the images. As there is a need to enhance the performance measures like compression ratio (CR) and peak signal to noise ratio (PSNR), an attempt is made in this research paper to analyze FIC using
Quad-tree Partitioning technique and Embedded Block Coding Optimization Truncation (EBCOT) technique for Medical Images. Here in this paper FIC with EBCOT is being implemented on Medical Images like CT of Bone and MR Images of Brain and thePerformance measures like CR, PSNR at different Threshold Values were measured. Mat lab simulated results for these Performance Measures showed that the PSNR and CR is high when FIC is performed along with EBCOT Encoding.
Filtering is Prime important processes in Medical Image processing applications. Any post processing process aims in the removal of unwanted noise which usually corrupts the image quality and perception. This research paper focuses on searching effective De noising filters for post processing of Fractal compressed Images on Medical Images like CT of Bone , MR Images of Brain ,Mammograms, Ultrasound Images of uterus. In this work Fractal Image Compression (FIC) a lossy compression scheme based on contractive mapping theorem is employed to map the Range blocks and Domain blocks by using the property of self similarity in the images. We have used two types of filters namely anisotropic diffusion filter and bilateral filter for the removal of noise in Medical images. The Peak signal to noise ratio (PSNR) was measured after applying the two different filters and a comparative analysis of PSNR values before and after filtering was recorded. The simulated results obtained showed an increase in PSNR value for bilateral filter than with anisotropic filter and also the quality of the image was improved.
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