Systolic array architecture for real-time Gabor decomposition

In this paper, we propose a combined systolic array - content addressable memory architecture for image compression using Gabor decomposition. Gabor decomposition is attractive for image compression since the basis functions match the human visual profiles. Gabor functions also achieve the lowest bound on the joint entropy of data. However these functions are not orthogonal and hence an analytic solution for the decomposition does not exist. Recently it has been shown that Gabor decomposition can be computed as a multiplication between a transform matrix and a vector of image data. Systolic arrays are attractive for matrix multiplication problems and content addressable memories (CAM) offer fast means of data access. For an n × n image, the proposed architecture for Gabor decomposition consists of a linear systolic array of n processing elements each with a local CAM. Simulations and complexity studies show that this architecture can achieve real-time performance with current technology. This architecture is modular and regular and hence it can be implemented in VLSI as a codec.