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Performance Analysis of Image Compression Using LPWCF

  • V. P. KulalvaimozhiEmail author
  • M. Germanus Alex
  • S. John Peter
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)

Abstract

Image compression is the most important feature for acheiving an efficient and secure data transfer. One of the main challenges in compression is developing an effective decompression. The input images that is compressed may not be more effectively restored in the decompression process that is based on quantization using Cosine Transformations or Wavelet transformations where the pixel information will be lost. To overcome these challenges, encoding process were employed. In the encoding process the pixel information were well protected but the compression efficiency is not improved. In order to overcome this challenge Lossless Patch Wise Code Formation (LPWCF) is employed. In the patch wise code generation the compression process is based on the pixel grouping and removing the relevant and recurrent pixels. In the proposed method, the images were first reduced in size by combining the current pixel with the previous pixel. The resulting image size is nearly the half of the size of the input image. The resulting image is then divided into small patches. In the patch recurrent pixels and their locations were identified. The identified pixel locations were placed prior to the pixel value and then the process is repeated for the complete image. The result of each patch acts as a code. In the receiver side the same process is reversed inorder to obtain a decompressed image. The process is completely reversible and hence the process can be employed in the process of transmission of the images. The performance of the process is measured in terms of the compression ratio, the image quality analysis of the input and the decompressed image based on PSNR, MSE and SSIM.

Keywords

Compression Pixel grouping Recurrent pixels Bits per pixel per band (bpppb) Decompression 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • V. P. Kulalvaimozhi
    • 1
    Email author
  • M. Germanus Alex
    • 2
  • S. John Peter
    • 3
  1. 1.Manonmaniam Sundaranar UniversityTirunelveliIndia
  2. 2.Department of Computer ScienceGovernment Arts CollegeNagercoilIndia
  3. 3.Department of Computer ScienceST. Xavier’s CollegePalayamkottaiIndia

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