Abstract
Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.
Highlights
Color image quantization plays an auxiliary, but still very important role in such tasks of color image processing as image compression, image segmentation, image watermarking, etc
The time complexity of the proposed quantization method based on K-Means with downsampling is O(d f · n · k · d · i ) [15], where: d f is a downsampling factor, n is the number of pixels, k is the number of expecting colors, d is the number of color components and i is the number of iterations
Generating a color palette was performed on all pixels of the image where the downsampling factor was 1/1 and on eight subsets of pixels obtained by Neighbor Interpolation (NNI) on the original image, where the downsampling factor was: 1/2, 1/4, 1/8, 1/16, 1/32, 1/64, 1/128 and 1/256
Summary
Color image quantization plays an auxiliary, but still very important role in such tasks of color image processing as image compression, image segmentation, image watermarking, etc. The high computational complexity of KM results from the necessity of carrying out a very large number of comparisons between input data. This is especially true for images that contain millions of pixels. The use of appropriate data structures and reducing the number of unnecessarily calculated distances between data allows for reducing the time required for KM [5] Another approach, useful in color quantization, can be the operation on a small sample of the image. An improved color quantization method may result in high image quality and short quantization time simultaneously.
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