Learning scalable dictionaries with application to scalable compressive sensing

Learned sparse signals representations led to state-of-the-art image restoration results for several problems in the field of image processing. In this paper we show that these are achievable for a compressive sensing (CS) scenario based on the sparsity pattern provided by learned dictionary specially designed for the scalable data representation. Experimental results demonstrate and validate the practicality of the proposed scheme making it a promising candidate for many practical applications involving both time scalable image/video display and scalable frame compressive sensing. Provided simulations involve CS scalable sparse recovery of dynamic data changing over time e.g., video. These are important for situations where video streams, tailored to the needs of a diverse user pool operating heterogeneous display equipment, are required. For the aforementioned purpose the proposed method outperforms the conventional K-SVD algorithm.