Lossy image compression algorithms that utilize non-uniform sampling and interpolation (NSI) of the image intensity surface create a lossy compression are asymmetrical, having a low decompression complexity and a potentially higher compression complexity. The algorithms non-uniformly sample the image data in two dimensions. The number of samples chosen, and hence the compression ratio, is based on a supplied error metric threshold and local image features. The technique uses a greedy sample point selection algorithm and then returns to the original sample point decisions and jitters them for a better fit. Decompression consists of a linear interpolation between sample points.

The original (uncompressed) Lena image

The Lena image compressed to 1.0 bit per pixel using NSI

A scaled plot of the difference between the original image data and the 1.0 bpp NSI compressed Lena image. Magnitude zero errors are represented by neutral gray

The white pixels indicate the position of sample points taken by NSI to compress the Lena image to 1.0 bpp

A set of experiments found this algorithm to be, on average, 2.5 dB worse in terms of PSNR than an image compressed by JPEG for the same bit rate. Decompression was significantly faster, 10x to 20x than a DCT based algorithm, however compression was two to three times slower.

A plot of the intensity data of line 266 of the original (uncompressed) Lena image

A plot of the intensity data of line 266 of the 1.0 bpp NSI compressed Lena image

For more detailed information, please refer to these publications:

[1] Walter Bender and Charles Rosenberg, "Image Enhancement Using Non-Uniform Sampling", SPIE Image Handling and Reproduction Systems Integration, vol. 1460, pp. 59-70, February 1991

[2] Charles Rosenberg, "A Lossy Compression Algorithm Based on Nonuniform Sampling and Interpolation of the Image Intensity Surface", SID International Symposium Digest of Technical Papers, vol. 21, pp. 388-391, September 1990

---

Hits on this page:

Last Modified :