Fixed, variable and adaptive bit rate data source encoding (compression) method
First Claim
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1. A method for vector encoding a data source, the method comprising:
- taking an observation of the data source;
estimating the probability density function from the data observation;
determining codepoints for vector encoding the data source based upon the estimated probability density function from the data source.
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Abstract
According to the invention, quantization encoding is conducted using the probability density function of the source, enabling fixed, variable and adaptive rate encoding. To achieve adaptive encoding, an update is conducted with a new observation of the data source, preferably with each new observation of the data source, preferably with each new observation of the data source. The current probability density function of the source is then estimated to produce codepoints to vector quantize the observation of the data source.
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Citations
14 Claims
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1. A method for vector encoding a data source, the method comprising:
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taking an observation of the data source;
estimating the probability density function from the data observation;
determining codepoints for vector encoding the data source based upon the estimated probability density function from the data source. - View Dependent Claims (2, 3, 4, 5, 6, 8)
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7. A method for encoding a non-stationary data source, the method comprising steps of:
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upon arrival of new data from the data source, determining a current estimate of probability density function model parameters of the data source by applying a re-estimation algorithm to a previous estimate of the model parameters and the new data; and
updating a set of codepoints using the model parameters.
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9. A method for encoding a non-stationary data source, the method comprising steps of:
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taking an observation Ω
k of a p-dimensional non-stationary random data source at time instant k;
modelling Ω
k as an independent and identically distributed realization of a parametric density, according to;
where, α
1 are non-negative constants andis referred to as cluster i and is an individual parameteric density paraneterized by Φ
i, m is the number of clusters, Φ
is the parameter set which defines a parametric model, Ω
k may be assumed to have been generated by one of the m clusters, and the probablity that a given obervation has been generated by cluster i is α
I;
estimating, by a density estimation algorithm, parametric model parameters of the parametric model from the current observation of the data source;
designing a separate codebook for each of the m clusters, allocating bits to clusters according to one of a fixed or variable rate;
then, upon each arrival of new data resulting in a new observation Ω
k-new, updating the codebook for each of the m clusters bydetermining a current estimate of parametric model parameters of the data source by applying a re-estimation algorithm to a previous estimate of the model parameters and the new data; and
updating the separate codebook for each of the m clusters using the model parameters. - View Dependent Claims (10, 11, 12, 13, 14)
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Specification