False Contour Reduction Using Neural Networks and Adaptive Bi-Directional Smoothing.

The larger display devices, the more noticeable artifacts such as false contours, block artifacts, and other types of noises. This paper proposes a false contour reduction algorithm using neural networks (NNs) and adaptive bidirectional smoothing. The proposed algorithm consists of two parts: false contour detection and reduction parts. In the false contour detection part, false contour candidate pixels are detected using the directional contrast features. The false contour reduction part is composed of two steps: NN processing and bi-directional filtering. In the first step, false contours are reduced by pixelwise processing using NNs. In the second step, bi-directional smoothing is applied to a neighboring region of the false contour. Computer simulations with several test images show the effectiveness of the proposed false contour reduction algorithm in terms of the visual quality of result images, edge maps detected by Sobel masks, the peak signal-to-noise ratio, the structural similarity, and the computation time. [ABSTRACT FROM AUTHOR]