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Related Experiment Video

Updated: Jul 7, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Line-based, reduced memory, wavelet image compression.

C Chrysafis¹, A Ortega

¹Hewlett-Packard Laboratories, Palo Alto, CA 94304, USA. chrysafi@hpl.hp.com

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

|February 8, 2008

Summary

This study introduces a novel line-based system for low memory wavelet image compression. It achieves high performance comparable to state-of-the-art methods while significantly reducing memory requirements.

Related Experiment Videos

Last Updated: Jul 7, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Area of Science:

Image Processing
Computer Vision
Data Compression

Background:

Wavelet transform and subband coding offer superior image compression compared to traditional methods.
Implementing these techniques in low memory environments without performance loss remains a challenge.

Purpose of the Study:

To develop a complete system for low memory wavelet image compression.
To address memory constraints in wavelet transform and coding processes.

Main Methods:

A line-based approach processes images line by line, minimizing memory usage.
A novel context-based encoder utilizes local wavelet coefficients, avoiding global information storage.
Synchronization between encoder and decoder memory is managed effectively.

Main Results:

The line-based wavelet transform achieves identical results to standard implementations.
The proposed low memory coder demonstrates performance comparable to state-of-the-art methods.
Memory utilization is significantly reduced compared to existing high-performance coders.

Conclusions:

The developed system offers an efficient solution for low memory wavelet image compression.
This approach enables high-performance image compression even with limited memory resources.
It overcomes previous limitations in implementing advanced compression techniques on memory-constrained platforms.