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Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Superpixel-Based PSO Algorithms for Color Image Quantization.

Mariusz Frackiewicz¹, Henryk Palus¹, Daniel Prandzioch¹

¹Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This study introduces IDE-PSO-CIQ, an enhanced particle swarm optimization algorithm for color image quantization. It achieves faster processing and comparable quality by utilizing superpixels and emotional state-based evolution.

Keywords:

clustering color image quantization computation rate image quality individual difference evolution particle swarm optimization superpixel

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Area of Science:

Artificial Intelligence
Computer Vision
Image Processing

Background:

Nature-inspired AI algorithms, particularly Particle Swarm Optimization (PSO), are established in Color Image Quantization (CIQ).
Existing PSO algorithms for CIQ can be computationally intensive.

Purpose of the Study:

To evaluate a novel modification of PSO for CIQ, termed IDE-PSO-CIQ, incorporating individual difference evolution based on particle emotional states.
To assess the efficiency and quality of IDE-PSO-CIQ, especially when applied to superpixel representations of images.

Main Methods:

Implementation and testing of the IDE-PSO-CIQ algorithm.
Comparison of IDE-PSO-CIQ against the standard PSO-CIQ algorithm using pixel, patch, and superpixel quality indices.
Application of both algorithms to superpixel image versions for accelerated color palette generation.
Development of a heuristic method for selecting the number of superpixels based on palette size.

Main Results:

IDE-PSO-CIQ demonstrated superiority over PSO-CIQ in quality indices.
Superpixel-based CIQ significantly reduced computation time for both algorithms.
The heuristic method for superpixel selection proved effective.
Quantized image quality using superpixels was high, only slightly inferior to pixel-based methods.

Conclusions:

IDE-PSO-CIQ offers an effective improvement for color image quantization.
Superpixel-based approaches drastically reduce CIQ computation time while preserving high image quality.
The proposed heuristic method aids in optimizing superpixel usage for efficient CIQ.