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Kamargaonkar, Chandrashekhar
- Image Compression Techniques Using Lossless Methods:A Review
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Affiliations
1 Shri Shankaracharya Group of Institution (SSGI), Bhilai-490028, C.G, IN
1 Shri Shankaracharya Group of Institution (SSGI), Bhilai-490028, C.G, IN
Source
Digital Image Processing, Vol 5, No 4 (2013), Pagination: 171-175Abstract
In recent years there has been an astronomical increase in the usage of computers for a variety of tasks. With the advent of digital cameras, one of the most common uses has been the storage, manipulation, and transfer of digital images. The files that comprise these images, however, can be quite large and can quickly take up precious memory space on the computer’s hard drive. In multimedia application, most of the images are in colour. And colour images contain lot of data redundancy and require a large amount of storage space. Image compression algorithms offer the means to minimize storage cost and to increase transmission speed. Compressing an image is significantly different than compressing raw binary data. Of course, general purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image are sacrificed for the sake of saving a little more bandwidth or storage space. This also means that lossy compression techniques cannot be used. Hence there is a need for efficient lossless schemes for medical image data. Lossless data compression is used in many applications. For example, it is used in the ZIP file format and in the UNIX tool gzip. It is also often used as a component within lossy data compression technologies (e.g. lossless mid/side joint stereo pre-processing by the LAME MP3 encoder and other lossy audio encoders). Several lossless compression schemes have been proposed. This paper provides a comparison and brief description about the various lossless compression schemes.Keywords
SPIHT, DICOM, Wavelets, JPEG, MPM Code, Multi-Scale Segmentation.
- Comparative Analysis of PCA, SPIHT and Haar Methods in Medical Image Compression
Abstract Views :162 |
PDF Views:4
Authors
Affiliations
1 F.E.T, SSTC, SSGI, Junwani, Bhilai, IN
1 F.E.T, SSTC, SSGI, Junwani, Bhilai, IN
Source
Digital Image Processing, Vol 10, No 4 (2018), Pagination: 53-60Abstract
Compression of medical image has acquired great attention attributable to its raising need to decrease the picture size while not compromising the diagnostically crucial medical data exhibited on the picture. PCA algorithmmay be used to help in image compression. Here PCA algorithm is characterized in two forms i.e. Standard PCA and Block-Based PCA. The block based PCA has 2 extended-PCA algorithms that manipulate the block data of the image are evaluated. The 1st algorithm is referred to as block-by-block PCA wherestandard PCA algorithm is utilized on every block of the picture. In the next algorithm- the block-to-row PCA, all of block data are initially concatenated into a row before the standard PCA algorithm is thereforeutilizedin the remodelled matrix. In this paper, the block based PCA and SPIHT primarily applied on the ROI region whereas General PCA and HAAR wavelet were applied to non-ROI region. An arbitrary shaped segmentation (Manual segmentation) is employed to trace the specified ROI on the image.The SPIHT is being compared with the block based PCA methods in terms of image quality and compression ratio while selecting either general PCA or HAAR wavelet on Non ROI. With this work, it’s observed that block-based PCA performs superior to the SPIHTwith regards toimage quality, producingsimilar compression ratio.
Keywords
Medical Image Compression, Principal Component Analysis (PCA), Block-Based PCA, Compression Ratio, Image Quality, HAAR, SPIHT.References
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