This paper introduces a novel design for the dictionary learning algorithm, intended for scalable sparse representation of high motion video sequences and natural images. The proposed algorithm is built upon the foundation of the K-SVD framework originally designed to learn non-scalable dictionaries for natural images. Proposed design is mainly motivated by the main perception characteristic of the Human Visual System (HVS) mechanism. Specifically, its core structure relies on the exploitation of the high-frequency image components and contrast variations in order to achieve visual scene objects identification at all scalable levels. Proposed design is implemented by introducing a semi-random Morphological Component Analysis (MCA) based initialization of the K-SVD dictionary and the regularization of its atom’s update mechanism. In general, dictionary learning for sparse representations leads to state-of-the-art image restoration results for several different problems in the field of image processing. In experimental section we show that these are equally achievable by accommodating all dictionary elements to tailor the scalable data representation and reconstruction, hence modeling data that admit sparse representation in a novel manner. Performed simulations include scalable sparse recovery for representation of static and dynamic data changing over time (e.g., video) together with application to denoising and compressive sensing.
- scalable video representation
- sparse coding
- contrast variation
- compressive sensing