Region of Interest Based Image Compression
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The main target of region of interest (ROI) based compression for medical image is to improve the compression efficiency for transmission and storage. Main goal of Region of interest(ROI) compression is to compress ROI with main quality as compared to other region call background. For an example, while compressing medical image the system important region should be compressed with better quality than background. Thus, the ROI area is compressed with less compression ratio and the background with the highest possible compression ratio in order to get overall better compression performance.
The aim of this paper is to propose an algorithm which compresses medical images. It requires some specific portion as region of interest called ROI in which we have to maintain the image quality and other than ROI portion is called Background. The US medical images are used for diagnosis purpose so here good quality of ROI is required. Image compression is the application of data compression on digital images. The objective is to reduce redundancies of image data in order to able to store or transmit the data in an efficient form. The reduction in file size allows more images to be stored in a given amount of disk or memory. It is also reduction the time required for images to be sent over the Internet. It also reduction the time for downloaded from Web pages. The purpose of image compression is to achieve a very low bit rate representation . In case of conventional compression schemes the equal loss of information will occur for whole image, as they are compressed with equal CR but in contextual compression schemes, the visual quality of important area (ROI) will be quite better due to less information loss of ROI as compared to back ground.
Previously we are used the image compression as lossless. This type of compression will not be support to all types of image formats. And we not specifying any particular region to compress particularly. So here the image will be compressed according to the image features. Si compression will come useless.
Here particularly we are using ROI(Region Of Interest) based image compression. The main advantage of ROI is we can mould the image compression to one particular area of compression. Instead of reducing all the pixel intensities we are using ROI to specify the required arrangements.
- Loss compression
- Required image compression
- Broadcast television
- Computer communications
- MATLAB 2014 or above versions
Mainly image compression will two types that loss and lossless image compressions. Most common we are using loss compression why because in all system applications which we generally using, will support jpg file formats. The jpg will support only support loss image compression. In this project also we are showing loss image compression by using ROI(Region Of Interest).
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