An algorithm for interpolation digital images and signals is developed. The proposed interpolator is based on context modeling. The prototype of the proposed interpolator is the NEDI algorithm (New Edge-Directed Interpolation). The proposed interpolator is adaptive, because different parameters of the interpolating function are used at different points in the image. The NEDI algorithm is modified so that it is able to process special non-redundant hierarchical grids of image pixels. This is necessary to embed the proposed interpolator in the hierarchical method of image compression. Computational experiments to investigate the effectiveness of the hierarchical compression method with the built-in interpolator proposed are carried out. Computational experiments prove that the proposed interpolator allows increasing the efficiency of the hierarchical method of image compression.
In this paper, a parameterized four direction contour-invariant extrapolator for differential pulse code modulation (DPCM) image compression is presented. Two calculation methods for extrapolation are available - linear and nonlinear one. Chosen method for extrapolation depends on the presence of a contour inside the vicinity of every processed pixel. Parameter responsible for the choice of calculation method is optimized before compression during recursive image size independent learning procedure. Computational experiments are carried out on a test set of images in order to examine proposed extrapolator. The advantage in terms of RMS error of proposed extrapolator over other ones on a certain types of images is shown.
Hierarchical interpolation of images is investigated in the problem of image compression. A new approach is proposed for optimizing the adaptive interpolator for hierarchical compression. This approach is based on optimizing the entropy of the compressed signal. This approach is more adequate to the compression problem than the known approach based on optimization of the interpolation error. The optimization algorithm for the adaptive interpolator is proposed on the basis of the proposed approach. The theoretical estimation of the computational complexity of the proposed interpolator is calculated. A comparison of this complexity with the complexity of other interpolators is performed. The advantage of the proposed interpolator over known interpolators is investigated experimentally. The win is calculated according to the size of the archive file. Recommendations for the use of the proposed interpolator are formulated.
Algorithms for detection of informative image fragments are proposed. Algorithms detect fragments of two types. Fragments of the first type («homogeneous fragments») are used to estimate the noise in the image. Fragments of the second type («stepped fragment») are used to estimate the frequency response of distortion system. Thus, these fragments can be used for assessment of image distortions and image quality in natural scenarios.
The adaptive nonlinear predictor is proposed for digital image compression method based on differential pulse code modulation. Greham predictor is parameterized for this. Proposed predictor works in different ways depending on the local image contours. A special feature is offered for estimation the contour direction and intensity in the neighborhood of the current pixel. The parameters of the proposed predictor are calculated by rapid training procedure before the actual compression. This procedure minimizes the sum of absolute values of prediction errors. Theoretical computational complexity of the proposed predictor is shown. Considered predictors are compared in real images by computational experiments. The win of proposed algorithm is demonstrated. In addition, the gain of compression method based on differential pulse code modulation with the proposed predictor against JPEG compression method is demonstrated.