Lapped vector quantization of images

Abstract

We present an improved decoding paradigm for vector quantization (VQ) of images. In this new decoding method, the dimension of the code vectors at the decoder is higher than the dimension of the input vectors at the encoder, so that the area covered by each output vector extends beyond the input block of pixels into its neighborhood. The image is reconstructed as an overlapping patchwork of output code vectors, where the pixel values in the lapped region are obtained by summing the corresponding elements of the overlapping code vectors. With a properly designed decoder code book, this lapped block-decoding technique is able to improve the performance of VQ by exploiting the interblock correlation at the decoder. We have developed a recursive algorithm for designing a locally optimal decoder code book from a training set of images, given a fixed VQ encoder. Computer simulation with both full-search VQ and pruned-tree-structured VQ encoders demonstrate that, compared to conventional VQ decoding, this new decoding technique reproduces images with not only higher SNR but also better perceptual quality.