Novel L1 PCA informed K-means color quantization

Lorenzo E. Jaques; Arthur C. Depoian II; Ethan Murrell; Dong Xie; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2619139

31 May 2022 Novel L₁ PCA informed K-means color quantization

Lorenzo E. Jaques, Arthur C. Depoian II, Ethan Murrell, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 120970H (2022) https://doi.org/10.1117/12.2619139
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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).

Conference Presentation

Citation Download Citation

Lorenzo E. Jaques, Arthur C. Depoian II, Ethan Murrell, Dong Xie, Colleen P. Bailey, and Parthasarathy Guturu "Novel L₁ PCA informed K-means color quantization", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 120970H (31 May 2022); https://doi.org/10.1117/12.2619139

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