Rate-distortion optimized color quantization for compound image compression

Wenpeng Ding, Yan Lu, Feng Wu, Shipeng Li

Abstract: In this paper, we present a new image compression scheme, which is specially designed for computer generated compound color images. First we classify the image content into two kinds: text/graphic content and picture content. Then two different compression schemes are applied blocks of different types. We propose a two stage segmentation scheme which combines thresholding block features and rate-distortion optimization. The text/graphics blocks compression scheme consists of two parts: color quantization and lossless coding of quantized images. The input images will first be color quantized and converted to codebooks and labels, introducing constraint distortion to the color quantization images. Then generated labels and codebooks are lossless compressed respectively. We proposed a rate-distortion optimized color quantization algorithm for text/graphic content, which introduces distortion to text content and minimizes the bit rate produced by the following lossless entropy compression algorithm. The picture content is compressed using conventional image algorithms like JPEG. The results show that the proposed scheme achieves better coding performance than other images compression algorithms such as JPEG2000 and DjVu.