Efficient computation of the Hutchinson metric between digitized images

• Published in: IEEE transactions on image processing Vol. 13; no. 12; pp. 1581 - 1588
• Main Authors: Drakopoulos, V., Nikolaou, N.P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-12-2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Cluster Analysis; Color; Compressed; Computational efficiency; Computational geometry; Computer Graphics; Computer Simulation; Decoding; Digitization; Exact sciences and technology; Fractal analysis; Fractals; Gray-scale; Hutchinson metric; Image coding; image comparison; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Imaging, Three-Dimensional - methods; Informatics; Information Storage and Retrieval - methods; Information, signal and communications theory; Mathematical models; Neural networks; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Shape; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal representation. Spectral analysis; Signal, noise; Subtraction Technique; Telecommunications and information theory; Testing; Performance evaluation; Error estimation; Image processing; Image compression; Decoding; Digital image; Image reconstruction; fractals; Image quality; Computational geometry; Image analysis; Image coding; Fractal; image comparison; Hutchinson metric; Comparative study
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2004.837550

The Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. An efficient solution to the problem of computing the Hutchinson metric between two arbitrary digitized images is considered. The technique proposed here, based on the shape of the objects as projected on the digitized screen, can be used as an effective way to establish the error between the original and the, possibly compressed, decoded image. To test the performance of our method, we apply it to compare pairs of fractal objects, as well as to compare real-world images with the corresponding reconstructed ones.