Abstract
The coverage of VQ has focused thus far on the coding of a single vector extracted from a signal, that is, on memoryless VQ where each input vector is coded in a manner that does not depend on past (or future) actions of the encoder or decoder. This vector is typically a set of parameters extracted from a finite segment of a signal or a set of adjacent samples of a signal. The segments are themselves usually blocks of consecutive samples of speech or two-dimensional blocks of pixels in an image. Usually we need to quantize a sequence of vectors where each vector can be assumed to have the same pdf, but the successive vectors may be statistically dependent. Separate use of VQ for each vector does not take this dependence into consideration. Vector coders possessing memory may be more efficient in the sense of providing better performance at given bit rates and complexity by taking advantage of the inter-vector or inter-block dependence or correlation.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer Science+Business Media New York
About this chapter
Cite this chapter
Gersho, A., Gray, R.M. (1992). Predictive Vector Quantization. In: Vector Quantization and Signal Compression. The Springer International Series in Engineering and Computer Science, vol 159. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3626-0_13
Download citation
DOI: https://doi.org/10.1007/978-1-4615-3626-0_13
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-6612-6
Online ISBN: 978-1-4615-3626-0
eBook Packages: Springer Book Archive