A neural network based predictor of filtering efficiency for image enhancement

Image filtering is widely used in remote sensing applications to improve object visibility or for other purposes. However, filtering does not always occur efficient enough and serving image enhancement purposes well. Thus, it is reasonable to have a simple but rather accurate predictor of filtering efficiency. Such a predictor can be based on statistics of DCT coefficients in image blocks. For improved prediction, we propose to apply several local statistics aggregated by a trained neural network. This way allows providing high accuracy prediction of image enhancement not only in terms of standard quality criteria but also in terms of metrics of image visual quality.