Constrained-Storage Vector Quantization with a Universal Codebook

— Many image compression techniques require the quantization of multiple vector sources with significantly different distributions. With vector quantization (VQ), these sources are optimally quantized using separate codebooks, which may collectively require an enormous memory space. Since storage is limited in most applications, a convenient way to gracefully trade between performance and storage is needed. Earlier work addressed this problem by clustering the multiple sources into a small number of source groups, where each group shares a codebook. We propose a new solution based on a size-limited universal codebook that can be viewed as the union of overlapping source codebooks. This framework allows each source codebook to consist of any desired subset of the universal codevectors and provides greater design flexibility which improves the storageconstrained performance. A key feature of this approach is that no two sources need be encoded at the same rate. An additional advantag...

• Type: Conference
• Year: 1995
• Where: DCC
• Authors: Sangeeta Ramakrishnan, Kenneth Rose, Allen Gersho
• Keywords: Computer Graphics, DCC 1995, Source Codebooks, Universal Codebook, Vector Quantization