A Novel Self-Creating Neural Network for Learning Vector Quantization
Neural Processing Letters
A VLSI architecture for 3-D self-organizing map based color quantization and its FPGA implementation
Journal of Systems Architecture: the EUROMICRO Journal
A Robust Parallel Architecture for Adaptive Color Quantization
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Color quantization using principal components for initialization of Kohonen Sofm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Journal of Real-Time Image Processing
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Several methods have been proposed to do color quantization for limited color displays. Self-organization of Kohonen feature maps (SOFM) is a very useful tool for data clustering. By extracting a special butterfly-jumping sequence from an image, a neural network was fed in that sequence of image data for SOFM training, and a fairly good color table was present to represent that image. The peak signal-to-noise ratio of the decoded image is high (about 35 dB in average for a 256 color image), and the perceptual quality is good as well, even with a small set of color tables (for example, 32 or 64 colors). Furthermore, the training process is fast. For encoding, we also propose an efficient algorithm based on the Kohonen map ordering property. By considering that the neighboring image pixels are closely related and by setting some acceptable thresholds, we can quickly get an encoded image. For further compression on the color indexed image with the limited color palette, we cut the indexed images into 4×4 blocks and send the block vectors into another SOFM neural network for training. Under the two-dimensional (2-D) mesh neural structure, SOFM vector quantization on an indexed image could largely reduce the color shift artifacts and avoid the requantization problem. About 0.5 b per pixel of coded image can be easily obtained with a fairly good perceptual quality. More importantly, the decoded color indexed images can be readily displayed. This will reduce the decoder complexity greatly