Multiple descent cost competition: restorable self-organization and multimedia information processing

Multiple descent cost competition is a composition of learning phases for minimizing a given measure of total performance, i.e., cost. In the first phase of descent cost learning, elements of source data are grouped. Simultaneously, a weight vector for minimal learning, (a winner), is found. Then, the winner and its partners are updated for further cost reduction. Therefore, two classes of self-organizing feature maps are generated: a grouping feature map, and an ordinary weight vector feature map. The grouping feature map, together with the winners, retains most of the source data information. This feature map is able to assist in a high quality approximation of the original data. In the paper, the total algorithm of the multiple descent cost competition is explained and image processing concepts are introduced. A still image is first data-compressed, then a restored image is morphed using the grouping feature map by receiving directions given by an external intelligence. Next, an interpolation of frames is applied in order to complete animation coding. Examples of multimedia processing on virtual digital movies are given