Deblocking and deringing are two video post-processing techniques largely used to remove coding artifacts and improve the visual quality when rendering low bit rate coded video. The algorithms used to achieve these tasks are computationally intensive and usually require high speed processors to be able to run in real time. Efficient implementations of signal adaptive filters for video post-processing can be obtained using the specialized features of the parallel BOPS(R) DSP cores. The performance achieved by deblocking and deringing CIF and SDTV size video sequences on the MANTA/sup TM/ prototype chip are illustrated. It is shown that such complex tasks may be executed at low clock rates using the BOPS ManArray/sup TM/ technology.

A new filtering architecture is proposed, generalizing some previously introduced multilevel median filters. An efficient design procedure for the new filtering architecture is demonstrated for image restoration application. Simulation results show a good noise rejection performance, combined with a fine detail preservation capability.

This paper proposes the use of mean absolute error (MAE) optimal Boolean and stack filters for sequential prediction in lossless grey-level image coding. FIR-Boolean hybrid filters are introduced as variations of Boolean filter structure and shown to be very effective for the prediction task. Different instances of optimal filtering are considered for realizing the prediction stage. First, the use of global-optimal predictors is analyzed, when the global MAE-optimal filter is used as a predictor. Then more refined structures, block-optimal and adaptive-size-block-optimal are considered, where predictors are adapted to local characteristics. These structures prove most suitable when small prediction masks are used. Extensive simulations are carried out for analyzing and comparing the performance of the newly introduced predictors and various other sequential predictors.

In this paper Boolean filters and a variation of these, FIR-Boolean hybrid filters are proposed for realizing the prediction stages of a multiresolution lossless image compression structure. Optimal and adaptive Boolean filters are used for prediction and the proposed predictors performance is compared to the performance of other lossless multiresolution methods: the hierarchical interpolation scheme (HINT) and the S+P transform.

We develop a new procedure for the optimal stack filter design under structural constraints, e.g. for minimizing an error criterion and simultaneously preserving the shape of some signals. The training framework for optimal stack filter design developed by Tabus, Petrescu and Gabbouj (see IEEE Transactions on Image Processing, Special Issue on Nonlinear Image Processing, IP-5, p.1-18, June 1996) provides us with a proper setting for imposing structural constraints to the optimal filter, in order to preserve some desired details of the image. The target application is optimal stack filter design for image restoration, the goal being the "most efficient" noise attenuation.

A training framework is developed in this paper to design optimal nonlinear filters for various signal and image processing tasks. The targeted families of nonlinear filters are the Boolean filters and stack filters. The main merit of this framework at the implementation level is perhaps the absence of constraining models, making it nearly universal in terms of application areas. We develop fast procedures to design optimal or close to optimal filters, based on some representative training set. Furthermore, the training framework shows explicitly the essential part of the initial specification and how it affects the resulting optimal solution. Symmetry constraints are imposed on the data and, consequently, on the resulting optimal solutions for improved performance and ease of implementation. The case study is dedicated to natural images. The properties of optimal Boolean and stack filters, when the desired signal in the training set is the image of a natural scene, are analyzed. Specifically, the effect of changing the desired signal (using various natural images) and the characteristics of the noise (the probability distribution function, the mean, and the variance) is analyzed. Elaborate experimental conditions were selected to investigate the robustness of the optimal solutions using a sensitivity measure computed on data sets. A remarkably low sensitivity and, consequently, a good generalization power of Boolean and stack filters are revealed. Boolean-based filters are thus shown to be not only suitable for image restoration but also robust, making it possible to build libraries of "optimal" filters, which are suitable for a set of applications.