Entropy Coding via Parametric Source Model with Applications in Fast and Efficient Compression of Image and Video Data

In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via maximum a posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques. The problem of optimal entropy coding for transmission of a block of data x
₁
,,x
₂
,...x
_N
, can be formulated by assuming that the data comes from a source with a parametric probability mass function (pmf) P(X
₁
,X
₂
,...XN;thetas) with parameter thetas (in general thetas is a vector). The parametric model assumption makes it possible to assign a probability to the event of observing x
₁
,,x
₂
,...x
_N
, and use this probability for entropy coding of this data, only by conveying the parameter thetas.The impressive results from the simple parametric model, based on a geometric distribution of coded data for compression of natural images, are encouraging to further investigate the effect of more complicated data models such as Poisson distribution and mixture models.