Image information splitting framework with importance sampling for robust transmission

In the traditional compression schemes of visual multimedia contents, such as video and image, scalable compression is usually achieved by splitting the information into one base layer and several enhanced layers. Such principle for scalable compression encounters a problem in the presence of transmission loss that the unpredictable loss in the base layer causes tremendous reduction of recognizable information by human perception. Multiple Description Coding (MDC) is defined to be robust in the presence of transmission loss and several MDC schemes have been proposed as extensions of the existing image and video compression standards. In this paper, a new presentation of image information based on the principle of MDC is proposed. The image is presented as samples whose density is variable according to the frequency energy. Moreover, the samples are able to be split into several partitions, promising that a recognizable approximation of the original content can be reconstructed by any partition, and more high frequency information can be reconstructed with more partitions, which fulfills the motivation of MDC. The reconstruction is realized by interpolating samples using normalized radial basis function (RBF) network. The result shows that the proposed framework of image information splitting provides acceptable quality scalability.