• Author(s): Kajal Sharma ; Nagresh Kumar
  • Paper ID: 1700651
  • Page: 176-182
  • Published Date: 27-04-2018
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 1 Issue 10 April-2018

Digital image compression deals with methods for reducing the total number of bits required to represent an image. This can be achieved by eliminating various types of redundancy that exist in the image dataset. Lossless image compression techniques preserve the information so that exact reconstruction of the image is possible from the compressed data. Major lossless or error free compression methods like Huffman, arithmetic and Lempel-Ziv coding do not achieve great compression ratio. It is necessary to preprocess the images in order to reduce the amount of correlation among neighboring pixels, to improve compression ratio further. Keeping this in view this works intend to focus on a comparative investigation of various lossless image compressions in spatial domain techniques and proposed an algorithm which achieves more compression ratio. The proposed algorithm is divided into two phases. In first phase the color image is divided into RGB plane and where each plane is divided into no. of blocks which uses variable bits to store each block pixels. Calculation of variable bits is dependent on pixel values of each block. In the second phase output of the first phase is supplied to LZW algorithm. The advantage of proposed scheme is that it uses integrated approach which depends upon pixels correlation within a block and LZW algorithm. The output of proposed scheme provides better compression than TIFF, GIF and PNG formats for color image.


LZW, RGB, Inter-pixel.


IRE Journals:
Kajal Sharma , Nagresh Kumar "SPATIAL DOMAIN LOSSLESS IMAGE DATA COMPRESSION" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 176-182

Kajal Sharma , Nagresh Kumar "SPATIAL DOMAIN LOSSLESS IMAGE DATA COMPRESSION" Iconic Research And Engineering Journals, 1(10)