Image Compression with Schauder Bases
As is known, color images are represented as multiple channels, i.e. integer-valued functions on a discrete rectangle corresponding to pixels on the screen. Thus, image compression can be reduced to investigating suitable properties of such functions. Each channel is compressed independently. We are representing each such function by means of multi-dimensional Haar and diamond bases so that the functions can be remembered by their basis coefficients without loss of information. For each of the two bases we present in detail the algorithms for calculating the basis coefficients and conversely, for recovering the functions from the coefficients. Next, we use the fact that both the bases are greedy in suitable Besov norms and apply thresholding to compress the information carried by the coefficients. After this operation on the basis coefficients the corresponding approximation of the image can be obtained. The principles of these algorithms are known (see e.g. ) but the details seem to be new. Moreover, our philosophy of applying approximation theory is different. The principal assumption is that the input data come from some images. Approximation theory, mainly the isomorphisms between Besov function spaces and suitable sequence spaces given by the Haar and diamond bases (see , ), and the greediness of these bases, are used only to choose a proper norm in the space of images. The norm is always finite and it is used for thresholding only.