In this paper it is presented that, 256 × 256 16 gray level images can be compressed fast and efficiently by using neural networks . Compression results were compared with the other methods according to mean square error (MSE) and visual of the image and it is seen that by further works on ANN the images can be compressed better.

https://www.mdpi.com/2297-8747/1/1/165/pdf

Comparing Artificial Neural Networks (ANN) Image Compression Technique with Different Image Compression Techniques

This paper presents a method for designing finite impulse response (FIR) filters for samples of a 2-D signal, e.g., an image, and its gradient. The filters, which are called blended filters, are decomposable in three filters, each separable in 1-D filters on subsets of the data set. Optimality in the minimum mean square error sense (MMSE) of blended filtering is shown for signals with separable autocorrelation function. Relations between correlation functions for signals and their gradients are derived. Blended filters may be composed from FIR Wiener filters using these relations. Simple blended filters are developed and applied to the problem of gray value image reconstruction from bilevel (scanned) clustered-dot halftone images, which is an application useful in the graphic arts. Reconstruction results are given, showing that reconstruction with higher resolution than the halftone grid is achievable with blended filters. >

/pdf/filters-involving-derivatives-with-application-to-5bmwi261fr.pdf

Filters involving derivatives with application to reconstruction from scanned halftone images

In this study we have attempted to investigate the stability problems observed in multichannel multidimensional linear predictive modeling of images. Morf et al.[3] have shown that based on a positive definite autocorrelation matrix, singular values of the matrix H ? ?q + 1.HERM(?q + 1) must lie inside the unit circle for a stable solution, where ?q + 1 is the normalized partial correlation matrix and HERM(.) denotes the Hermitian operator. We have employed this stability method to modify the multichannel Levinson algorithm [1,2] for obtaining stable linear prediction coefficients. Since the procedure involved block-by-block processing of image intensity values, blocks of 32x32 pixels were defined as analysis windows. A two-step stabilization method has been developed for these windows and it is applied to the multichannel multidimensional linear prediction of monochromatic imagery. The first step is based on heuristic notions and employed for obtaining strictly positive definite multichannel autocorrelation matrices R[q]. The second step is based on forcing singular values of H to reside inside the unit circle for satisfying the stability criterion reported in [3].

Y. Ozturk

Papers

Multichannel linear prediction and applications to image coding