BLIND ESTIMATION OF ADDITIVE NOISE VARIANCE FOR NOISY SIGNALS IN DCT DOMAIN

Abstract

In many practical situations, signals are corrupted by noise and it is desired to apply filtering to remove it. For most of modern filters, it is needed to know noise variance a priori or to pre-estimate it in a blind manner in the presence of signal component. Requirements to methods of blind estimation are formulated and it is shown that it is difficult to satisfy these requirements. Based on the used model of additive white Gaussian model, a method operting in DCT domain is considered and thoroughly studied. The choice of test signals is motivated. Local estimates obtained on blocks of different size are studied and it is demonstrated that these local estimates, although based on robust estimates of data scale, can be sufficiently influence by signal component that leads to a certain percentage of large amplitude DCT coefficients in data sample. It is then shown that such abnormal local estimates have to be rejected (or their influence on the final estimate should be minimized). This is done by robust processing of local estimates. It is established that block size considerably influences accuracy characterized by bias of estimates and their variance. The role of bias is dominant – noise standard deviation is overestimated - and the main task is to decrease it. According to experiments carried out for ten variants (parameter sets) of estimation method, the best results are, on the average, obtained if block size is equal to 32 and local estimates are processed using sample median. Computational efficiency is analyzed and it is shown that processing can be done quite quickly. This allows expecting real-time implementation for such applications as electrocardiogram and speech processing

Keywords

• автоматическая оценка
• белый шум
• дисперсия помех
• дкп
• робастность
• отношение сигнал-шум
• тестовые сигналы