Links to other people's research which has utilized the Lena image are welcome. The goal of this work was to create a lossy compression algorithm which was asymmetrical, having a low decompression complexity and a potentially higher compression complexity. The algorithm non-uniformly samples 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.
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.