A Reconstruction Algorithm with Multiple Side Information for Distributed Compression of Sparse Sources
We consider the task of reconstructing target signals which are processed as sparse sources for a distributed compression scenario, where communication between the sources is prohibited, however, correlation of information among sources can be utilized at the decoder. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI) for such distributed sparse sources. The proposed algorithm takes advantage of both a compressive sensing (CS) reconstruction with SI and an iteratively weighted ℓ1-norm minimization by solving a general weighted multi-ℓ1 (or n-ℓ1) minimization. To utilize the known multiple SIs, the algorithm computes optimal weights on not only each individual SI but among SIs where the weights are adaptively updated according to changes at every iteration of the reconstruction. By this optimization, the proposed reconstruction algorithm with multiple SI (RAMSI) can robustly exploit the multiple SIs with different qualities. We experimentally demonstrate our algorithm on compressing feature histograms as sparse sources which are extracted from a multi-view image database for multi-view recognition. The results show that the RAMSI with multiple SIs efficiently outperforms the ℓ1 minimization and also the CS reconstruction with only one SI.