IMAGE DATA COMPRESSION AND RECONSTRUCTION BASED ON FREQUENCY DOMAIN CONSIDERATIONS AND PARTIAL DIFFERENTIAL EQUATION MODELS

[摘要] The image data compression and reconstruction problems are investigated in this dissertation, and new techniques for their solution are presented. Conventional image transform coding techniques are given a spatial domain interpretation. Based on this interpretation, an improvement using a larger transform size and an overlap-and-save scheme is presented for the Fourier transform which possesses simple spatial domain features. Next, spatial domain processing techniques based on frequency domain analysis are discussed. Two-dimensional digital filters are used to implement the compression and reconstruction scheme. McClellan;;s transformation and two fast approximation schemes are used to preserve the spectral properties of the image. To obtain a low mean-square-error, spatial constraints are added. Finally, a generalized spline interpolation approach based on partial differential equation image models is introduced. It is shown that this deterministic design is congruent to the stochastic minimum-mean-square-error estimation of the images given the decimated samples. Two implementation schemes, the cardinal spline filtering and the locally based B-spline fitting, are presented. The results are compared with the conventional Lagrange, cubic B-spline interpolation and the transform coding techniques. The various developments presented are demonstrated by computer simulations performed on a Genisco graphics display system supported by a PDP-11/55 computer.

[发布机构] Rice University

[效力级别] Electrical engineering