The mappings from multidimension to one dimension, or the inverse mappings, are theoretically described by space filling curves, i.e., Peano curves or Hilbert curves. The Peano Scan is an application of the Peano curve to the scanning of images, and it is used for analyzing, clustering, or compressing images, and for limiting the number of the colors used in an image. In this paper an efficient method for visual data compression is presented, combining generalized Peano Scan, wavelet decomposition, and adaptive subband coding technique. The Peano Scan is incorporated with the encoding scheme in order to cluster highly correlated pixels. Using wavelet decomposition, an adaptive subband coding technique is developed to encode each subband separately with an optimum algorithm. Discrete Cosine Transform (DCT) is applied on the low spatial frequency subband, and high spatial frequency subbands are encoded using Run Length encoding technique.