Comparison of generalized Gaussian and Laplacian modeling in DCT image coding
作者： R.L. Joshi , T.R. Fischer
DOI: 10.1109/97.386283
关键词: Peak signal-to-noise ratio 、 Discrete cosine transform 、 Gaussian process 、 Mathematics 、 Pattern recognition 、 Gaussian function 、 Modified discrete cosine transform 、 Algorithm 、 Gaussian random field 、 Artificial intelligence 、 Gaussian 、 Gaussian blur
摘要: Generalized Gaussian and Laplacian source models are compared in discrete cosine transform (DCT) image coding. A difference peak signal to noise ratio (PSNR) of at most 0.5 dB is observed for encoding different images. We also compare maximum likelihood estimation the generalized density parameters with a simpler method proposed by Mallat (1989). With block classification based on AC energy, densities DCT coefficients much closer or even Gaussian. >
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