Extrapolation of Incomplete Image Data with Discrete Orthogonal Transforms

In image processing and transmission, interpolation and extrapolation are of great importance whenever missing pixels have to be filled in, and many methods have been proposed to solve this problem. In this paper we present a method for extrapolating the missing data with an existing set of basis functions of a selected orthogonal transform. The best extrapolation is found according to linear approximation theory as a weighted sum of basis functions, where coefficients of the sum are solutions of the derived matrix equation

A Three-Level Hierarchical Encoder Using Shape Independent Transform

In this paper a scheme for utilizing shape independent basis functions for a hierarchical multiresolution image compression is shown. First, an image is segmented and its segments\' boundaries are polygon approximated, thus achieving an image mask. Second, this image mask and the image are used as an input of a three-level hierarchical encoder. The hierarchical encoder subsamples the image and the image mask and encodes them shape independently; it produces an output bit stream on a respective level that is also used on lower level(s) for further coding. On the base level a triangulation of the image mask is performed for superior performance. Another compression mode is, hence, introduced for the shape independent transform coding

A Novel Technique of Error Concealment Method Selection in Texture Images Using ALBP Classifier

There are many error concealment techniques for image processing. In the paper, the focus is on restoration of image with missing blocks or macroblocks. Different methods can be optimal for different kinds of images. In recent years, great attention was dedicated to textures, and specific methods were developed for their processing. Many of them use classification of textures as an integral part. It is also of an advantage to know the texture classification to select the best restoration technique. In the paper, selection based on texture classification with advanced local binary patterns and spatial distribution of dominant patterns is proposed. It is shown, that for classified textures, optimal error concealment method can be selected from predefined ones, resulting then in better restoration. For testing, three methods of extrapolation and texture synthesis were used

Application of Shape-Independent Orthogonal Transforms for Image Interpolation

In the contribution we develop a new method for object-oriented interpolation of images. It is an important tool in image processing, since using interpolation we can considerably decrease amount of data, necessary for image reconstruction. Application of this interpolation enables to down-sample the object separately. The selected object can be processed at the different sampling level. This approach allows object oriented zoom, for example. Moreover, object - oriented approach is a very novel idea that helps to understand the content of image. Method is created from cosine approximation implemented to coding with shape - independent basis functions

Throughput Analysis of an Adaptation Rule in the HARQ Environment

In this paper we analyze the adaptation rule, which estimates the channel state and switches between hybrid ARQ (automatic-repeat-request) and pure ARQ. Convolutional code was chosen as FEC (forward-error-correction) in hybrid ARQ part and go-back-N ARQ scheme is used in both cases. The adaptation rule is based on counting ACKs and NAKs and its throughput analysis is made

Segmentation Based Image Scanning

The submitted paper deals with separate scanning of individual image segments. A new image processing approach based on image segmentation and segment scanning is presented. The resulting individual segments 1-dimensional representation provides higher neighbor pixel similarity than the 1-dimensional representation of the original image. This increased adjacent pixel similarity was achieved even without application of different recursive 2-dimensional scanning methods [4], such as Peano-Hilbert scanning method [1]. The resulting 1-dimensional image representation provides a good base for applying lossless compression methods, such as the entropic coding. The paper contains also results analysis of the traditional method scanned segment pixels and adjacent pixel differences from the entropy point of view. As these results indicate the lossy compression methods could be applicable using this approach as well and might improve the final results as confirmed by simple prediction algorithm results presented in this paper. More complex and sophisticated lossy compression algorithms application will be a part of the future work

A Comparison of Selected GBN ARQ Schemes for Variable-Error-Rate Channel Using QAM

In non-stationary channels, error rates vary considerably. The paper compares Yao's Adaptive Go-back-N (GBN) Automatic-Repeat-Request (ARQ) scheme with Adaptive go-back-N with sliding window mechanism which both estimate the channel state in a simple manner, and adaptively switch their operation mode. The throughput of these schemes is compared in conditions of Additive White Gauss Noise (AWGN) channel with independent errors using 16-QAM modulation

Error Concealment using Neural Networks for Block-Based Image Coding

In this paper, a novel adaptive error concealment (EC) algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison