Novel L1 PCA informed K-means color quantization
Lorenzo E. Jaques, Arthur C. Depoian, Ethan Murrell, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu · 2022
In recent years, the computational power of handheld devices has increased rapidly to the point of parity with computers of only a generation ago. The multiple tools integrated into these devices and the progressive expansion of cloud storage have created a need for novel compressing techniques for both storage and transmission. In this work, a novel L1 principal component analysis (PCA) informed K-means approach is proposed. This new technique seeks to preserve the color definition of images through the application of K-means clustering algorithms. Assessment of the efficacy is carried out utilizing the structural similarity index (SSIM).