Entropy-constrained product code vector quantization with application to image coding

M. Lightstone, D. Miller, S. K. Mitra

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


While product code VQ is an effective paradigm for reducing the encoding search and memory requirements of vector quantization, a significant limitation of this approach is the heuristic nature of bit allocation among the product code features. We propose an optimal bit allocation strategy for PCVQ through the explicit incorporation of an entropy constraint within the product code framework. Unrestricted entropy-constrained PCVQs require joint entropy codes over all features and concomitant encoding and memory storage complexity. To retain manageable complexity, we propose "product-based" entropy code structures, including independent and conditional feature entropy codes. We also propose an iterative, locally optimal encoding strategy to improve performance over greedy encoding at a small cost in complexity. This approach is applicable to a large class of product code schemes, allowing joint entropy coding of feature indices without exhaustive encoding. Simulations demonstrate performance gains for image coding based on the mean-gain-shape product code structure.

Original languageEnglish (US)
Article number413389
Pages (from-to)623-627
Number of pages5
JournalProceedings - International Conference on Image Processing, ICIP
StatePublished - 1994
EventThe 1994 1st IEEE International Conference on Image Processing - Austin, TX, USA
Duration: Nov 13 1994Nov 16 1994

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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