A DATA PROCESSING METHOD

1 - 24 . (canceled)

25 : A computer-implemented data processing method to improve information quality in data sequences by attenuating noise in the data sequences, the method comprising:

receiving, from at least one sensor, input data sequences having a plurality of elements, wherein each of the elements has at least one dimensional component;

independently performing, by a processor, a spectral analysis on the at least one dimensional component of each of the elements to estimate a signal profile of the input data sequences;

estimating, by the processor, a noise profile of the input data sequences using calibration data associated with the at least one sensor;

dynamically calculating, by the processor, a time-constant for a noise attenuation filter, and adapting the time-constant over time, for each one of the elements in the input data sequences, based on a relationship between the noise profile and the signal profile;

applying, by the processor, the noise attenuation filter for each one of the elements to each respective one of the elements to filter the input data sequences to derive filtered data sequences; and

outputting the filtered data sequences.

26 : The computer-implemented data processing method of claim 25 , wherein for at least one of the plurality of elements, the at least one dimensional component of that element comprises a temporal component.

27 : The computer-implemented data processing method of claim 26 , wherein the at least one dimensional component further comprises a spatial component.

28 : The computer-implemented data processing method of claim 25 , wherein for at least one of the plurality of elements, the at least one dimensional component of that element comprises a temporal component derived from that at least one dimensional component.

29 : The computer-implemented data processing method of claim 25 , wherein the noise attenuation filter comprises a low-pass filter having the time-constant.

30 : The computer-implemented data processing method of claim 25 , further comprising estimating a signal-to-noise ratio of the input data sequences based on the relationship between the noise profile and the signal profile.

31 : The computer-implemented data processing method of claim 30 , further comprising:

comparing the signal-to-noise ratio to a minimum target signal-to-noise ratio, and

dynamically calculating the time-constant based on a result of the comparison of the signal-to-noise ratio and the minimum target signal-to-noise ratio.

32 : The computer-implemented data processing method of claim 31 , wherein the signal-to-noise ratio is compared to the minimum target signal-to-noise ratio by dividing the signal-to-noise ratio by the minimum target signal-to-noise ratio to obtain a filter value, and dynamically calculating the time-constant is based on the filter value such that a first filter value results in a first range of filtering and a second, greater filter value results in increasing amounts of filtering proportional to that filter value.

33 : The computer-implemented data processing method of claim 32 , wherein the time-constant has a maximum time-constant limit corresponding to a threshold filter value.

34 : The computer-implemented data processing method of claim 30 , further comprising:

dynamically calculating a further time-constant for a further noise attenuation filter based on a trend of the signal-to-noise ratio over time, and

applying the further noise attenuation filter to smooth the time-constant over time.

35 : The computer-implemented data processing method of claim 34 , wherein, responsive to the trend of the signal-to-noise ratio increasing over time, the further time-constant is decreased, and, responsive to the trend of the signal-to-noise ratio decreasing over time, the further time-constant is increased.

36 : The computer-implemented data processing method of claim 35 , wherein the further noise attenuation filter comprises a low-pass filter having the further time-constant.

37 : The computer-implemented data processing method of claim 25 , further comprising dynamically compressing a dynamic range of the filtered data sequences by applying an input gain to the filtered data sequences to derive corrected filtered data sequences.

38 : The computer-implemented data processing method of claim 37 , further comprising:

determining an adaptation level from the time-constant over time, and

determining the input gain using the adaptation level, wherein a first input gain is associated with first adaptation levels and a second, smaller input gain is associated with second, higher adaptation levels.

39 : The computer-implemented data processing method of claim 38 , further comprising:

estimating a skewness of the input data sequences by determining an amplitude modulation of the adaptation level across the input data sequences, and

determining a magnitude of the input gain based on the skewness.

40 : The computer-implemented data processing method of claim 39 , further comprising scaling the magnitude of the input gain between a minimum input gain and a maximum input gain.

41 : The computer-implemented data processing method of claim 39 , further comprising dynamically compressing the corrected filtered data sequences by applying a dynamic gamma correction having a gamma correction factor to the corrected filtered data sequences to derive compressed filtered data sequences.

42 : The computer-implemented data processing method of claim 41 , further comprising calculating the gamma correction factor based on the skewness.

43 : The computer-implemented data processing method of claim 41 , further comprising dynamically compressing the compressed filtered data sequences by applying a further dynamic gamma correction having a further gamma correction factor to the compressed filtered data sequences to derive further compressed filtered data sequences, wherein calculating the further gamma correction factor is based on the skewness.

44 : The computer-implemented data processing method of claim 43 , further comprising scaling the compressed filtered data sequences to a designated bandwidth to derive output data sequences by applying a designated gain based on a historical midpoint value of bandwidth usage of the compressed filtered data sequences.

45 : The computer-implemented data processing method of claim 25 , wherein the input data sequences are video data of any modality and the elements comprise pixels.

46 : The computer-implemented data processing method of claim 25 , wherein the input data sequences are video data of any modality and the elements comprise at least one of color and wavelength channels.

47 : The computer-implemented data processing method of claim 25 , wherein the input data sequences are audio data of any modality and the elements comprise at least one of spectrograms and frequency bands derived from the audio data.

48 : The computer-implemented data processing method of claim 25 , wherein the filtered data sequence has a non-uniform gain applied thereto.