Parametric context adaptive Laplace distribution for multimedia compression
share
Data compression often subtracts predictor and encodes the difference (residue) assuming Laplace distribution, for example for images, videos, audio, or numerical data. Its performance is strongly dependent on proper choice of width (scale parameter) of this parametric distribution, can be improved if optimizing it based on local situation like context. For example in popular LOCO-I (JPEG-LS) lossless image compressor there is used 3 dimensional context quantized into 365 discrete possibilities treated independently. This article discussed inexpensive approaches for exploiting their dependencies by using ARCH-like context dependent models for parameters of parametric distribution for residue, also evolving in time for adaptive case. For example tested such 4 or 11 parameter models turned out providing similar performance as 365 parameter LOCO-I model for 48 tested images. Beside smaller headers, such reduction of number of parameters can lead to better generalization, and allows to practically exploit higher dimensional contexts, for example using information from all 3 color channels, further pixels, or of some additional region classifiers.
READ FULL TEXT