D. Petrescu

Efficient implementation of video post-processing algorithms on the BOPS parallel architecture

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.

/spl Lscr/-M-S filters for image restoration applications

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.

Prediction based on Boolean filters for multiresolution lossless image compression

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.

Prediction based on Boolean, FIR-Boolean hybrid and stack filters for lossless image coding

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.

Multilayer Boolean and stack filter design

This paper analyzes the properties of some layered structures formed by cascading layers of Boolean and stack filters and solves the optimal design problem using techniques developed under a training framework. We propose a multilayer filtering architecture, where each layer represents a Boolean or a stack filter and the outputs of the intermediate filtering layers provide some partial solutions for the optimization problem while the final solution is provided by the last layer output. The approach to the optimal design is based on a training framework. Simulations are provided to show the effectiveness of the proposed algorithms in image restoration applications.

Solutions for optimal Boolean and stack filter design under a training framework

This paper introduces a training framework for the optimal nonlinear filter design problem. The problem to be solved within the present framework is the selection of the best filter under a data dependent criterion (rather than a model dependent criterion) in one class of nonlinear filters. A class of filters, namely Boolean filters is then considered, for which holds a decoupling property, allowing to transform the initial integer valued problem into the binary domain. The equivalence between the original criterion (in the integer signal domain) and a criterion expressed in the binary signal domain is shown then to hold. The procedures for obtaining the optimal solution for two classes of nonlinear filters, Boolean filters and stack filters, are derived under the new framework. Some numerical simulations are provided, in order to illustrate the effectiveness of the procedures in solving the noise rejection problem.<<ETX>>