Towards the Synergy between Compression and Content-Based Analysis: A Pattern-Driven Approach

This paper presents a novel pattern-driven image compression technique for exploring the synergy between content-based analysis and compression. Within the pattern-driven paradigm, image data are considered as relational and classifiable entities, which are low level visual patterns including: (1) flat or homogeneous patterns (HP), (2) structural lines, curves and boundaries indicating the intensity/structural discontinuities (SD), and (3) complex/composite patterns (CP). Concisely, the pattern-based image model / can be loosely defined as f_image = f_HP U f_SD U f_CP .