Binary Merge Coding for Lossless Image Data Compression

: Problem statement: Image processing applications were drastically increasing over the years. In such a scenario, the fact that, the digital images need huge amounts of disk space seems to be a crippling disadvantage during transmission and storage. So, there arises a need for data compression of images. Approach: This study proposed a novel technique called binary merge coding for lossless compression of images. This method was based on spatial domain of the image and it worked under principle of Inter-pixel redundancy reduction. This technique was taken advantage of repeated values in consecutive pixels positions. For a set of repeated consecutive values only one value was retained. Results: The proposed binary merge coding achieved the compression rate of the brain image was 1.6572479. Comparatively, it is 100% more than the compression rate achieved by standard JPEG. Conclusion/Recommendations: This technique was simple in implementation and required no additional memory area. The experimental results of binary merge coding were compared with standard JPEG and it showed that, the binary merge coding improved compression rate compared to JPEG. The same algorithm can be extending to color images. This algorithm can also used for lossy compression with few modifications.


INTRODUCTION
Data Compression is a technique of encoding information using fewer bits than an un encoded representation would use through specific encoding or compression algorithms [2,4] . All forms of data which includes text, numerical and image contain redundant elements. Through compression, the data can be compressed by eliminating the redundant elements The History of image data compression started probably about a half of century ago with the works on predictive coding and variable length codes [6] . The technological breakthrough that took place in 60's, 70's and 80's resulted in efficient compression algorithms [8] that have been standardized in early 1990's and currently are in common use together with the improvements achieved during the last decade. These advances have brought substantial increase in efficiency of earlier basic techniques. Nevertheless, the last decade was also a period of strenuous search for new technologies of image data compression [7] .
Image compression technique is divided into two major categories [3] , which lossless compression technique and Lossy compression technique. In lossless compression, no information is lost and the decompressed data are identical to the original uncompressed data. While in Lossy compression, the decompressed data may be an acceptable approximation to the original un compressed data [5] .
This study focuses Loss less compression and proposed Binary merge coding Technique which works under the principle of removing Inter-pixel redundancy in spatial domain of the image

MATERIALS AND METHODS
Proposed method: Binary Merge Coding is based on spatial domain of the image and is suitable for compression of medical images [1] . The main objective of this technique is to take advantage of repeated values in consecutive pixels positions. For a set of repeated consecutive values only one value is retained.
In the binary merge coding two codes are used to build the bit plane [1] . The codes are as given below: • Code 1 (one) is used to indicate that current pixel is different from previous pixel. In this case the current pixel is moved to the data table • Code 0 is used to indicate that the current pixel is exactly same as previous pixel. This eliminates the storage of current pixel After generating and merging the Bit Plane and data table, Huffman coding is applied to generate final form of compressed file.
In Binary Merge Coding Compression and Reconstruction model, as shown in Fig. 1

RESULTS AND DISCUSSION
The brain image which is taken as one sample source image, its histogram and statistical information are as shown in the Fig. 3. The histogram gives the distribution of the pixels in the range 0-255.
The generated results after executing Binary Merge Coding are shown in the Table 1. The memory requirement for BMC technique is very less compare to JPEG, because the processing is done byte by byte. In the case of JPEG the entire image needs to be brought into memory. As per as the process complexity is concerned, the Binary Merge Coding is simple to implement comparatively JPEG. The graph in Fig. 4 is drawn as the results shown in the Table1.    Image  RAW  --------------------------------------------------------

CONCLUSION
The compression rate of Binary Merge Coding is better than JPEG in medical images The reconstructed image matches 100% with the original image because this is loss less Image compression technique.
The memory requirements for processing the images in this technique are significantly less compared to JPEG. The JPEG technique requires more memory because the entire image needs to be brought into memory. But for the Binary Merge Coding some sizable amount memory is required, because they process the image pixel by pixel.
The Binary Merge Coding is also applied for color images, which is producing equally good results in comparison with monochrome images.