Color image compression and limited display using self-organization Kohonen map

Abstract

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/spl times/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.