The idea of corner representation is by M.I. Schlesinger from the end of eighties and it is not widely known. I will explain the idea on the simplest case - one single binary image. The image is losslessly compressed by representing it by means of residuals of the non-linear predictor spanning the window 2x2 pixels only. The main benefit comes from the fact that set operations, translation, scaling, connected components labelling, and skeletonisation can be performed on compressed images directly. The performance will be demonstrated on the program written within a project by the team of 5 students earlier this year.
Image is treated as stacked bit-planes.
The probe of a predictor is larger and adaptive. The achieved compression ratios and decompression speed outperforms most of the recent known methods. The possibility to work directly with compressed images is lost. The corner representation can be generalized into more dimensions. We have shown that the possibility to perform operations in compressed form is preserved.