Image scalability using wavelet vector quantization

Abstract

Spatial scalability is a feature referring to image representation in different sizes and is particularly useful in image browsing, as well as in progressive image transmission applications. We propose a new technique for scaling images based on combined wavelet transform and vector quantization (VQ). The proposed technique exploits the spatial scalability feature of wavelet transform and the efficient compression performance of vector quantization. Although VQ is a powerful technique for low bit rate image compression, the label stream VQ is not scalable. Hence, we first propose a scalable VQ algorithm (SVQ) where a smaller-size, coarse quality image can be obtained by decoding a portion of the label bitstream. This image can be further enhanced in size by progressively decoding the remaining bits of the label bitstream. We note that to ensure partial decodability of VQ labels, a multiresolution codebook structure is required. We then apply the SVQ technique in the wavelet domain (WSVQ) where the input image is wavelet-decomposed into three levels and the resulting coefficients are organized into vectors to exploit the intra- and interband correlations. The interband correlations are exploited by employing a nonlinear interpolative vector quantization. Simulation results confirm the superior subjective performance of the proposed technique at a significant reduced computational complexity.