Multimodal Compression (MC) is a new approach that we have developed in order to encode jointly data, acquired from numerous modalities. For example, one can encode jointly and image and a set of signals. Generally speaking, signal samples are merged in the spatial domain of the image, by avoiding the Region Of Interest (ROI) ,using a specific mixing function (not to be confused with watermarking). Afterwards, the whole mixture is compressed using any standard such as JPEG 2000. The spatial mixing function inserts samples in a selected region after a down-sampling process. The decoding is achieved by inverting the process using a separation function. Results show that this technique allows better performances in terms of Compression Ratio (CR) compared to approaches which encode separately modalities. This apporach has been evaluated for medical data and extended to other types of image and signals. This topic was studies through two thesis.
Thesis 1: Supervisors: Prof. Amine Nait-ali, co-supervisor: Dr. R. Fournier.
Thesis 2: Supervisors: Prof. Amine Nait-ali, co-supervisor: Dr. R. Fournier.