Lossless Compression Algorithm Using Improved RLC for Grayscale Image

• Published in: Arabian Journal for Science and Engineering Vol. 41; no. 8; pp. 3061 - 3070
• Main Authors: Anantha Babu, S., Eswaran, P., Senthil Kumar, C.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-08-2016
• Subjects: Algorithms; Coding; Compression ratio; Engineering; Format; Humanities and Social Sciences; Image compression; Image quality; Matlab; multidisciplinary; Research Article - Computer Engineering and Computer Science; Science; Lossless compression; Improved RLC (I-RLC); Run-length coding (RLC); Resource sharing; Non-overlapping; Block matrix
• ISSN: 1319-8025; 2191-4281
• DOI: 10.1007/s13369-016-2082-x

Image compression plays a major role in video and audio image processing. Lossless image compression is used to reduce the volume of image data without compromising the image data quality. Keeping this demand in mind, the researchers around the world are attempting to attain a high compression ratio to modern communication technology. In the network computing, resource sharing and storing the image data has been a great challenge to computer society. The existing run-length coding (RLC) supports only for binary image and fails to give the reasonable compression ratio of non-repetition pixels. The source symbols are reduced by applying improved run-length coding (I-RLC) algorithm to get a high compression ratio. The proposed I-RLC model gets the original image and converts into a matrix format. The large-size matrix is divided into a number of non-overlapping small size block matrix. The small size blocks are carried out to achieve the compression ratio. This same process repeats the whole image block size. The proposed I-RLC algorithm is applied for different image formats to find out which method gives better results and quality. Whenever the proposed I-RLC method is applied to the sub-block wise, the computation time for 256 × 256 blocks takes 1.31 ms, while the block sizes reduced to 4 × 4, its timing is 0.01 ms. The proposed I-RLC algorithm is implemented for gray scale image. Finally, the compression ratio table is generated. The proposed I-RLC algorithm is tested and implemented through various parameters such as MSE, SNR and PSNR by using MATLAB Version 2013a.