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Related Experiment Video

Updated: Jun 4, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Stationary probability model for bitplane image coding through local average of wavelet coefficients.

Francesc Aulí-Llinàs¹

¹Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, Spain. fauli@deic.uab.es

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 18, 2011

Summary

This study presents a new probability model for bitplane image coding, using wavelet transform signals. The model enhances coding efficiency and parallelism by estimating wavelet coefficients via local averages.

Related Experiment Videos

Last Updated: Jun 4, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Area of Science:

Digital image processing
Signal processing
Information theory

Background:

Bitplane image coding is crucial for efficient data compression.
Wavelet transforms are widely used in image compression standards like JPEG2000.
Existing models may not fully capture the statistical properties of wavelet transform signals.

Purpose of the Study:

To introduce a novel probability model for symbols in bitplane image coding.
To leverage precise characterization of wavelet transform signals for improved modeling.
To enhance coding efficiency, parallelism, and spatial scalability in image compression.

Main Methods:

Characterizing signals produced by wavelet transforms.
Estimating wavelet coefficient magnitudes using the local average of neighbors.
Assuming emitted bits are under-complete representations of the signal.
Integrating the local average-based probability model within the JPEG2000 framework.

Main Results:

The proposed model offers enhanced coding efficiency compared to existing methods.
The model provides increased opportunities for parallel processing in image coding.
Improved spatial scalability is achieved through the new probability model.
The system, while not JPEG2000 compatible, retains standard features.

Conclusions:

The local average-based probability model is effective for bitplane image coding.
This approach offers significant practical benefits for image compression systems.
The model represents a valuable advancement in wavelet-based image coding techniques.