Color Compression of RGB Image Based on K-means Clustering
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Mudan Lv, Xiao Wang, Yining Xue, Li Yu, Mingjie Jin
In life, the amount of information in an image is often very large. Color images usually contain thousands of colors, which brings great inconvenience to people's storage and transmission efficiency. Therefore, the image compression method is gradually being viewed by all. Under the premise of lossy compression, this paper focuses on the influence of RGB image before and after color compression on absolute mean error, establishes K-means clustering model, randomly selects the initial clustering center, and performs image color compression.
K-means clustering method, initial clustering center, color compression