As more and more applications of digital
image processing have to combine image compression and highly automated image analysis, it
becomes of critical importance to study the interrelations existing between image
compression and feature extraction.
To elucidate on this situation we first present a systematic comparison of contemporary
general purpose lossy image compression techniques with respect to fundamental features,
namely lines and edges detected in images. A representative set of benchmark edge
detection and line extraction operators is applied to original and compressed images
resulting in clear guidelines which combination of compression technique and edge
detection algorithm is best used for specific applications.
On the other hand we introduce a new method for lossy image compression which exhibits
special advantages when robust compression behavior in the vicinity of lines and edges is
needed. Comparing to other general purpose compression techniques, our approach preserves
important image characteristics more precisely while simultaneously proving less
susceptible to the introduction of annoying artifacts. Therefore, our approach can be
expected to be of considerable interest for a wide range of integrated computer vision
applications, particularly including areas like teleoperation and robotics.