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Updated: Jun 11, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

An improved image compression algorithm using binary space partition scheme and geometric wavelets.

Garima Chopra¹, A K Pal

¹College of Basic Sciences and Humanities, G. B. Pant University of Agriculture and Technology, 263145 Pantnagar, India. garima.chopra@gmail.com

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

|July 10, 2010

Summary

This summary is machine-generated.

This study enhances geometric wavelet (GW) image coding using a novel binary space partitioning scheme. The improved method achieves superior performance compared to existing wavelet and sparse geometric representation algorithms.

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Last Updated: Jun 11, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Geometric Wavelet (GW) is an advanced technique for multivariate nonlinear piecewise polynomial approximation.
Existing image compression methods, including wavelet-based and sparse geometric representation algorithms, have limitations in efficiency and performance.

Purpose of the Study:

To improve the Geometric Wavelet (GW) image coding method.
To introduce a novel binary space partition scheme utilizing slope-intercept representation for enhanced image compression.

Main Methods:

The enhanced Geometric Wavelet (GW) algorithm employs slope-intercept representation within a binary space partition scheme.
Performance evaluation involved comparisons with established methods: Embedded Zerotree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT), Embedded Block Coding with Optimized Truncation (EBCOT), Bandelets, and the original GW algorithm.

Main Results:

The proposed image compression algorithm demonstrates superior performance over EZW, Bandelets, and the original GW algorithm.
A specific improvement of 0.22 dB was recorded over the GW method at a compression ratio of 64 for the Cameraman test image.

Conclusions:

The enhanced Geometric Wavelet (GW) method, utilizing slope-intercept representation, offers significant improvements in image compression.
This approach represents a promising advancement in sparse geometric representation for efficient and high-quality image coding.