A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images

Area of Science:

• Computer Science
• Data Compression
• Image Processing

Background:

• String transformation techniques are crucial for data compression.
• Existing methods like Burrows-Wheeler Transform (BWT) are widely used.
• Novel approaches are needed to improve compression efficiency for image data.

Purpose of the Study:

• To introduce and evaluate a new string transformation technique named Move with Interleaving (MwI).
• To compare MwI's performance against established methods for 2D raster image data.

Main Methods:

• Developed the Move with Interleaving (MwI) string transformation technique.
• Implemented four distinct 2D-to-1D data arrangement methods: scan-line, left-right, strip-based, and Hilbert.
• Conducted experiments on 32 benchmark greyscale raster images of varying resolutions.

Main Results:

• The proposed MwI transformation significantly reduces information entropy in greyscale raster images.
• MwI demonstrated comparable entropy reduction to the combination of Burrows-Wheeler Transform (BWT) and Move-To-Front (MTF) or Inversion Frequencies (IF).
• The Hilbert arrangement within MwI yielded superior results compared to other tested arrangements and transformations.

Conclusions:

• Move with Interleaving (MwI) is a promising new technique for string transformation and data compression.
• The Hilbert arrangement is particularly effective when used with MwI for image data.
• MwI offers a competitive alternative to existing compression pre-processing techniques.