Using Visual Metrics to Selecting ICA Basis for Image Compression: A Comparative Study


In order to obtain a good image compression result, it would be appropriate to previously estimate the error between a distorted image and its reference in such a process. Traditionally, the Mean-Squared Error (MSE) has been used as a standard measure for evaluate the effect of dimensionality reduction methods. More recently, other measures for assessing perceptual image quality has been proposed in the literature. In this paper, the main interest relies on a comparative study between the MSE and the Structural Similarity Index (SSIM), which uses structural similarity as a principle for measuring image quality. The basic aiming for such study is the proposal of an ordering and selecting procedure of the transformation basis found by Independent Component Analysis (ICA), which can take one of these measures into account. The principal motivation for this idea is that, in contrast to Principal Component Analysis (PCA), ICA does not have a property that allows a natural ordering for its components (called ICs). For evaluating the efficiency of such approach, a comparative study between PCA and the ICA-based proposal is also carried out for an image dimensionality reduction application. It can been noted that the ICA method, when using hyperbolic tangent function, could provide an efficient method to select the best ICs.

In Advances in Artificial Intelligence
Patricia R. Oliveira
Henrique Morimitsu
Esteban F. Tuesta