Scalable image compression using combined wavelet transform and vector quantization
Sethuraman Panchanathan, Amit Kumar Jain, N. Gamaz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In this paper, we propose vector quantization (VQ) based scheme for scalable image compression. Scalability is a generic feature referring to image representation in different sizes (spatial scalability) and/or picture qualities (SNR scalability). VQ is a powerful technique for low bit rate image compression. However the conventional VQ approach does not provide a scalable bitstream. We propose a VQ based algorithm (SVQ) to achieve spatial scalability. In SVQ technique, a pyramidal structure of three layers is built by applying 2D wavelet downsampling filters on the input image. The label stream is then made scalable such that a smaller-size image can be obtained by decoding a portion of the bitstream. This image can be further enhanced in size by progressively decoding the remaining bits. This algorithm ensures partial decodability of VQ labels by using separate codebooks one for each spatial resolution. We then propose a combination of wavelet transform and SVQ technique called WSVQ to exploit the cross-correlations among wavelet sub-bands. Simulation results confirm the substantial reductions in bit rate and superior subjective image quality at each spatial resolution using the proposed algorithm, at a significantly reduced computational complexity.