We describe a new technique for image encoding in which Gaussian-like operators serve as the basis functions. The representation differs from established techniques in that the Gaussian code elements are localized in both space and spatial frequency.
Pixel to pixel correlations are first removed by subtracting a low-pass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance, and the low-pass filtered image may be represented at reduced sample density. Further data compression is achieved by quantizing the difference image and repeating the en-coding process for the low-pass filtered image.
The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus the code tends to enhance salient image features. A primary advantage of the present code is that it is well suited for many image analysis tasks as well as for data compression. Fast algorithms are described for coding and decoding.