Efficient Color Quantization Using Superpixels
Abstract
:1. Introduction
2. Materials and Methods
3. Experiments and Results
3.1. Testing of the Proposed Methods
3.2. The Impact of Image Resolution on Computation Rate
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CQ | Color quantization |
CIELab | Lab color space defined by International Commission on Illumination |
DSCSI | Directional statistics color similarity index |
FCM | fuzzy c-means |
FSIMc | Color feature similarity index |
HPSI | Haar wavelet perceptual similarity index |
KM | k-means |
KM++ | k-means++ |
MC | Median cut |
PSNR | Peak signal-to-noise ratio |
RGB | Red, green, blue |
SLIC | Simple linear iterative clustering |
SLIC++ | Simple linear iterative clustering++ |
SPFCM | Superpixel version of fuzzy c-means |
SPKM | Superpixel version of k-means |
SPMC | Superpixel version of Median cut |
SPSIM | Superpixel similarity index |
SSIM | Structural similarity index measure |
WSNR | Weighted signal-to-noise ratio |
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Frackiewicz, M.; Palus, H. Efficient Color Quantization Using Superpixels. Sensors 2022, 22, 6043. https://doi.org/10.3390/s22166043
Frackiewicz M, Palus H. Efficient Color Quantization Using Superpixels. Sensors. 2022; 22(16):6043. https://doi.org/10.3390/s22166043
Chicago/Turabian StyleFrackiewicz, Mariusz, and Henryk Palus. 2022. "Efficient Color Quantization Using Superpixels" Sensors 22, no. 16: 6043. https://doi.org/10.3390/s22166043
APA StyleFrackiewicz, M., & Palus, H. (2022). Efficient Color Quantization Using Superpixels. Sensors, 22(16), 6043. https://doi.org/10.3390/s22166043