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Hybrid Adaptive Lossless Image Compression Based on Discrete Wavelet Transform.

Roman Starosolski1

  • 1Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel hybrid transform for lossless image compression, combining discrete wavelet transform (DWT) with prediction. This method significantly enhances compression ratios for both photographic and screen content images compared to standard JPEG 2000.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Discrete Wavelet Transform (DWT) is a standard for image compression.
  • Simple prediction methods are often ineffective with DWT.
  • Reversible Denoising and Lifting Steps (RDLS) with step skipping can modify DWT subbands.

Purpose of the Study:

  • To develop a new hybrid transform for lossless image compression.
  • To effectively combine DWT with prediction for improved compression ratios.
  • To propose practical compression schemes with varying tradeoffs.

Main Methods:

  • A hybrid transform integrating DWT and prediction was developed.
  • Prediction was applied to DWT subbands modified by RDLS with step skipping.
  • Image adaptivity, heuristics, and entropy estimation were used for transform construction.
Keywords:
JPEG 2000discrete wavelet transformentropy estimationhybrid transformlossless image compressionpredictive codingreversible denoising and lifting stepscreen content codingstep skippingtransform coding

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Main Results:

  • The hybrid transform effectively combined DWT with prediction.
  • Prediction nearly doubled compression ratio improvements from RDLS with step skipping.
  • One proposed scheme improved photographic and non-photographic image compression ratios by 1.2% and 30.9% respectively, with minimal modifications.

Conclusions:

  • The proposed hybrid transform offers significant improvements in lossless image compression.
  • RDLS with step skipping is key to effectively combining DWT and prediction.
  • Practical compression schemes provide flexible tradeoffs between compression ratio and computational cost.