Title :
Comparison of generalized Gaussian and Laplacian modeling in DCT image coding
Author :
Joshi, Rajan L. ; Fischer, Thamas R.
Author_Institution :
Sch. of Electr. & Comput. Eng., Washington State Univ., Pullman, WA, USA
fDate :
5/1/1995 12:00:00 AM
Abstract :
Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding. A difference in peak signal to noise ratio (PSNR) of at most 0.5 dB is observed for encoding different images. We also compare maximum likelihood estimation of the generalized Gaussian density parameters with a simpler method proposed by Mallat (1989). With block classification based on AC energy, the densities of the DCT coefficients are much closer to the Laplacian or even the Gaussian.<>
Keywords :
Gaussian processes; Laplace equations; discrete cosine transforms; image classification; image coding; maximum likelihood estimation; source coding; DCT coefficients; DCT image coding; PSNR; block classification; discrete cosine transform; generalised Gaussian source models; generalised Laplacian source models; generalized Gaussian density parameters; maximum likelihood estimation; peak signal to noise ratio; Art; Discrete cosine transforms; Image coding; Laplace equations; Maximum likelihood estimation; Parameter estimation; Shape; Statistical analysis; Testing;
Journal_Title :
Signal Processing Letters, IEEE
