PREDICTING THE EFFICIENCY OF DCT-BASED DENOISING OF 1-D SIGNALS CORRUPTED BY ADDITIVE WHITE GAUSSIAN NOISE

The object of the study is the process of 1-D signal processing by means of DCT-based filter. The subject of the study is the method for prediction of filtering efficiency in terms of signal-to-noise ratio improvement. The goal of the study is to identify which parameters can be used for prediction, evaluate the potential accuracy of the predictions, and assess whether the proposed approach is sufficiently generalizable. Methods used: numerical simulation, verification for a set of test 1-D signals of different origins. Results obtained: (1) accurate prediction is feasible, with a high level of accuracy achieved; (2) prediction accuracy depends on an input parameter that can be computed relatively easily; and (3) the proposed approach is sufficiently general to be applicable to both speech and medical signals affected by additive white Gaussian noise. Conclusions: (1) If the input SNR is below 30 dB, DCT-based filtering with appropriately chosen parameters can enhance it; (2) the extent of this improvement varies significantly but is predictable; and (3) this predictability enables informed decisions about whether filtering is beneficial and how to optimally configure its parameters.