Effective Multiple Vector Quantization for Image Compression

Multiple-VQ methods generate multiple independent codebooks to compress an image by using a neural network algorithm. In the image restoration, the methods restore low quality images from the multiple codebooks, and then combine the low quality ones into a high quality one. However, the naive implementation of these methods increases the compressed data size too much. This paper proposes two improving techniques to this problem: "index inference" and "ranking based index coding." It is shown that index inference and ranking based index coding are effective for smaller and larger codebook sizes, respectively.