Rank-order polynomial subband decomposition for medical image compression

Area of Science:

• Digital image processing
• Data compression
• Signal processing

Background:

• Progressive lossless image coding is crucial for efficient data handling and transmission.
• Existing methods like SPIHT, WTCQ, and JPEG have limitations in compression efficiency and progressive browsing.
• Morphological Subband Decomposition (MSD) provided a foundation for advanced techniques.

Purpose of the Study:

• To introduce and evaluate a novel nonlinear decomposition method for progressive lossless image compression.
• To compare the performance of the proposed method against established image coding schemes.
• To explore the potential for hybrid lossy/lossless compression using the new technique.

Main Methods:

• Development of Rank-Order Polynomial Decomposition (ROPD), a generalization of MSD.
• Implementation of ROPD utilizing polynomial prediction models for subband decomposition.
• Comparative analysis of ROPD against SPIHT, WTCQ, and JPEG codecs.
• Evaluation of embedded bitstream functionality for data browsing.
• Application of ROPD for hybrid compression of ultrasound images.

Main Results:

• ROPD achieves comparable or slightly superior lossless compression rates to SPIHT and WTCQ.
• The proposed ROPD method significantly outperforms the standard JPEG codec.
• ROPD offers improved lossless compression rates and superior browsing quality over MSD.
• Hybrid lossy/lossless compression demonstrated potential gains, especially for regions of interest.

Conclusions:

• ROPD is an effective nonlinear decomposition technique for progressive lossless image coding.
• The method provides enhanced compression efficiency and browsing capabilities.
• ROPD shows promise for advanced applications, including hybrid compression of medical images.