Coding of surveillance imagery for interpretability using local dimension estimates
Robert Prandolini · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
This paper introduces a novel image coding principle: the coding of an image to maximize its interpretability versus bit-rate performance. For large surveillance images it would be more appropriate if the encoded wavelet coefficients were prioritized in their order of importance for interpretability. This paper presents one method for such a system. The importance values are derived from the estimates of the local dimension in image regions, which is a measure on the local image dynamics. The scale of the area used for the estimates is dyadic and maps to the image scale-space. The wavelet coefficients from a Mallat decomposition are prioritized according to their importance, based on the local information dimension estimates. Subjective evaluations have shown that this importance prioritization schema is preferred over the traditional progressive PSNR optimal approach. The paper will discuss the implementation of an importance prioritization schema for the EBCOT image coder, which is the algorithm used in JPEG2000. The concept of importance prioritization for interpretability may benefit future low bit-rate image and video coding.