Mean-gain-shape vector quantization

  • Authors:

  • Karen L. Oehler;Robert M. Gray

  • Affiliations:

  • Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA

  • Venue:
  • ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
  • Year:
  • 1993

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Abstract

Mean-gain-shape (MGS) is a product code formulation of vector quantization (VQ) which allows flexible bit allocation between the mean, gain, and shape features of a vector. Product codes provide memory and complexity advantages over non-product VQ. This makes the use of larger vectors more feasible, and increasing vector dimension improves compression efficiency. The product code presented here obtains the minimum distortion reproduction vector by successive encoding in each of the three codebooks. We use pruned tree-structured vector quantizers (PTSVQ) to provide variable rate codes at low encoding complexity. Simultaneous pruning of the three codebooks provides optimal bit allocation. Prediction and concatenation is used to take advantage of interblock correlation. The results compare favorably with other tree-structured VQ methods.