International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 04 | Apr -2017
p-ISSN: 2395-0072
www.irjet.net
Lossless Optimal Compression of Medical Data Using Adaptive Golomb Rice Encoding Sailendra Kumar Verma1, Sandeep Kumar2, Pranav Tripathi3 1,2,3UG
Scholar Department of CSE, GCET, Gr. Noida, Uttar Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Image compression is a widely used to reduces
the storage and communication overhead during the transmission of the files but there is always risk of information loss in this process. Lossless compression is an approach to achieve the significant compression ratio without losing any information about the file. Which encourages the Remote Medical Monitoring system, it is an application of telemedicine, in which we provide the digital medical support easily to the disaster areas using compressed data transmission. Compression is the process of reducing the elements of the data which does not affect the necessary properties of the data. In this paper we are proposing the modifications in the lossy techniques to achieve the lossless compression. The technique uses the blocks of optimal size to prevent data lose with lower increment in computation. IntDCT is used to get frequency information and to perform quantization. Modified Golomb-Rice code are used for further encoding the coefficients of the data.
image separately to do so we are performing color-space conversion of the image from RGB to YCbCr. Because human eyes are more sensitive to the luminance factor so we can perform the down sampling of the chrominance attributes. After this we take matrix of 4 Ă— 4 and perform all the operations on these blocks of the image. First we use IntDCT to convert the images in to frequency domains in this process we use a standard matrix to find the pixels frequency values. Next, we perform quantization the real compression is performed here because most of the less effectives coefficients become zero in this process. We separate both AC and DC coefficients and use zig - zag and differential encoding respectively further these coefficients are encoded using the modified Golomb-Rice codes. The reverse process is performed at the receiver ends to get the information from the compressed data to get the actual data. The complete procedure followed for compression is shown in Figure 1.
Key Words : Lossless Image Compression, Golomb-Rice Code, Discrete Cosine Transform, Quantization, Color-space Conversion.
1. INTRODUCTION Image Compression can be used as an efficient approach in Remote medical monitoring (RMM) system based primary health care (PHC) system which is an application of the telemedicine. PHC provides the fast medical support in disaster areas where physical medical support cannot be easily provided. PHC optimally compresses the large medical data of patients and send it to the care centres. Doctors at the care centres , will analyze and send correct prescription back to the PHC through the fast transmission medias like WANET. We can perform lossless compression of the data in the text format .In this paper we proposed the technique to perform the lossless compression of the data which is in the image format. Various techniques are present which can perform the lossless compression of images but sometimes in medical process we cannot afford the loss of information in the medical data of patients. In the proposed technique we will first get the luminance and chrominance attributes of the Š 2017, IRJET
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Impact Factor value: 5.181
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Fig - 1: Image Compression Process
2. Proposed Medical Technique
Data
Compression
JPEG (Joint Photographic Experts Group) compression is the most popular compression technique for images. In lossy JPEG compression, pixels are first transformed using discrete cosine transform (DCT), then quantized using quantization tables, and finally encoded using Huffman coding. In the proposed technique we perform modification in size of block element and use the Golomb-Rice codes for encoding to get the lossless compression. ISO 9001:2008 Certified Journal
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