Feature-based image compression with steered Hermite transforms

We propose that the Hermite transform can bridge the gap between conventional transform coding techniques and second-generation coding techniques that make use of explicit descriptions of perceptually important image structures (e.g., contours and lines). This claim is motivated by two important characteristics of the Hermite transform. First, it shows good image analysis properties in detecting and extracting (local) image features such as orientation and position. Second, extracted image features can be used to steer the Hermite transform into a form with high energy compaction properties.