Adaptive Image Sampling and Interpolation for Data Compression

A new interpolation algorithm for 2D data is presented that is based on the least-squares optimization of a spline-like scheme. This interpolation technique is then integrated into a two-source decomposition scheme for image-data compression. First, an optimum least-squares bilinear interpolation is implemented and applied to a uniform image sampling. Second, the problem of spatial adaptivity is addressed in order to fully exploit the compression capability of the technique. The solution lies in a nonuniform sampling strategy based on a recursive splitting algorithm. Experimental results show that the proposed image-interpolation algorithm is very efficient. The major advantages of this scheme over traditional block-coding methods are the absence of the tiling effect (thanks to the continuity constraints imposed on the reconstruction process) and a more effective exploitation of interblock correlation. © 1994 Academic Press. All rights reserved.