HVS based dictionary learning for scalable sparse image representation

A novel dictionary learning design, driven by the Human Visual System (HVS) perception characteristic, for scalable representation of natural images is proposed. It builds upon the K-SVD algorithm, which learns non-scalable dictionaries for natural images. We introduce regularization over the K-SVD dictionary atom update stage, enabling scalable sparse image reconstruction. Mainly, emphasis is on the dictionary´s low and high spatial frequency components. Experimental results demonstrate the practicality of the proposed scheme for effective scalable sparse recovery of dynamic data changing over time (e.g., video). For the aforementioned purpose the proposed method outperforms the conventional K-SVD algorithm on average by 10.8[dB].