Image Compression using Run Length Encoding

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The goal of image compression is to remove the redundancies by minimizing the number of bits required to represent an image. It is used for reducing the redundancy that is nothing but avoiding the duplicate data. It also reduces the storage memory to load an image. Image Compression algorithm can be Lossy or Lossless. In this paper, DWT? based image compression algorithms have been implemented using MATLAB platform. Then, the improvement of image compression through Run Length Encoding (RLE) has been achieved. The three images namely Baboon, Lena and Pepper have been taken as test images for implementing the techniques. Various image objective metrics namely compression ratio, PSNR and MSE have been calculated. It has been observed from the results that RLE based image compression achieves higher compression ratio as compared with DWT? based image compression algorithms.

Image compression is important for many applications that involve huge data storage, transmission and retrieval such as for multimedia, documents, videoconferencing, and medical imaging. Uncompressed images require considerable storage capacity and transmission bandwidth. The objective of image compression technique is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form.

This results in the reduction of file size and allows more images to be stored in a given amount of disk or memory space. Image compression can be lossy or lossless. not provide sufficiently high compression ratios to be truly useful in image compression. Lossless image compression is particularly useful in image archiving as in the storage of legal or medical records. Methods for lossless image compression includes: Entropy coding, Huffman coding, Bit-plane coding, Run-length coding and LZW ( Lempel Ziv Welch ) coding.

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