Image compression and recovery through compressive sampling and particle swarm

Abstract

Abstract-We present an application of particle swarm techniques to the problem of sparse signal recovery. Although a direct application of particle swarm is straightforward, specifics of the signal recovery problem can be incorporated into particle behavior in a way that substantially improves the quality of the recovered signal. With encouraging results for synthetic signals, we apply this technique to the problem of image compression, where typical image blocks can be expected to exhibit many very small elements under a transformation like the DCT. In this application, we observe that better results are obtained by first forcing image blocks to be sparse rather than compressively sampling blocks that are approximately sparse.

Unexpected emergent behaviors from elementary swarms

“…Applications include economics [3], inverse problems [8], [14], [21], communications [1], [6], [7], [15], [18], optimization [9], [19], warfare [4], image processing [23], pattern recognition [27], [28], business [3], acoustics [25], national security [16], [17] and search algorithms [1], [20].…”