A multiple vector quantization approach to image compression

This paper investigates the effectiveness of a parallelized approach to VQ based image compression. In particular, we consider an image compression method using multiple VQs. The method, called MVQ, generates multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, MVQ restores low quality images from the multiple codebooks, and then combines the low quality ones into a high quality one. Further, we present an effective coding scheme for codebook indexes to overcome the in-efficiency of MVQ in compression rate. Our simulation results show that the MVQ method outperforms a conventional single-VQ method when the compression rate is smaller than some values.