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Review of Computer Engineering Research

August 2016, Volume 3, 2, pp 35-40

Lossless Image Compression and Decompression to Improve the PSNR and MSE Values Using Architecture

S. Kannadhasan


B.Naveen Lingaesh


R. Alagumanikandan

S. Kannadhasan 1

B.Naveen Lingaesh 2 R. Alagumanikandan 2 

  1. Assistant Professor, Department of Electronics and Communication Engineering, Raja College of Engineering and Technology, Madurai, Tamilnadu, India 1

  2. U.G Student, Department of Electronics and Communication Engineering, Raja College of Engineering and Technology, Madurai, Tamilnadu, India 2

Pages: 35-40

DOI: 10.18488/journal.76/2016.3.2/

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An adaptive algorithm for compressing the color images is proposed. This technique uses a combination of simple and computationally easy operations. The two main steps consist of decomposition of data and data compression. The result is a practical scheme that achieves good compression while providing fast decompression. The approach has performance comparable to and often better than, existing architecture. This paper gives the overview of an adaptive lossless compression scheme. This scheme uses a new technique to predict a  pixel by matching neighboring pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy while handling red and blue samples and an adaptive codeword generation technique to encode the prediction residues. The technique lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real time multimedia system. Lossless compression algorithm is used for this technique. A low Complexity predictive model is proposed using the correlation of pixels and color components. Also a color space transform is used and good decoration is obtained in our algorithm. The compared experimental results have shown that our algorithm has a noticeably better performance than traditional algorithms.

Contribution/ Originality


Preprocessor, DDPCM, Golombrice encoder, Decoder.



  1. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, "Image denoising using a scale mixture of Gaussians in the wavelet domain," IEEE Transactions on Image Processing, vol. 12, pp. 1338–1351, 2003.
  2. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Transactions on Image Processing, vol. 15, pp. 3736–3745, 2006.


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This study received no specific financial support.

Competing Interests:

The authors declare that they have no competing interests.


All authors contributed equally to the conception and design of the study.

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