METHOD AND APPARATUS TO DETECT THE PRESENCE OF NOISE

Abstract: The invention concerns a method and apparatus for detecting noise in an electrical signal. The invention is of particular application to the detection of noise in an electrical signal which represents an input video signal. The input signal is subjected to a frame delay and the absolute difference of each frame compared to the succeeding frame is derived by a difference circuit. The absolute difference signal from the difference circuit is sampled and the samples are applied to four comparator gates each of which selects samples having a signal level corresponding respectively to the gate. The outputs from the gates are counted in four accumulators which correspond respectively to the gates. The accumulated count values are supplied to a microprocessor which fits a probability density curve to the count values and determines whether a local maximum exists in the curve within a selected range of signal values. The presence of such a local maximum is indicative of the presence of noise in the input signal.

Description: MFTHOD AND APPARATUS TO DETECT THE PRESENCE OF NOISE The present invention relates to a method and apparatus to detect noise in a succession of samples of an electrical signal and is particularly adapted to detecting noise in samples of an electrical signal representing a sequence of images. Image sequences are often impaired by small levels of noise. If a television camera is used under adverse lighting conditions, for example, the output signal level from the camera can drop to such an extent that the level of source noise becomes noticeable and causes unwanted distortion of the signal. Other examples where noise levels can build up are in multi- generation analogue video tapes or in the transmission of video signals over signal channels with low signal to noise ratios. This noise, particularly if its level is unknown, can cause problems in signal processing stages subsequent to the source of the noise. If the signal is a video signal which is to be digitally compressed, for example, the presence of noise can lead to picture artefacts such as "blockiness" if the transmission bandwidth is not high enough. Other examples where it is important to get a measure of the amount of noise in a video signal occur in video processing algorithms based on exploiting temporal frame difference signals. Such video processing algorithms include adaptive noise reduction algorithms. The statistical properties of noise are quite similar to those of video signals. Indeed noise with appropriate spectral shaping is often used as a first approximation to model video signals. It is therefore not surprising that algorithms for the detection of source noise in video signals are often unreliable in so far as they can not distinguish low levels of noise from video signals which occur in fine texture. The present invention seeks to provide a method and apparatus for distinguishing the presence of noise in samples of an electrical signal irrespective of whether the signal represents a sequence of images including low-level detail. According to the present invention there is provided a method of detecting noise in a succession of samples of an electrical signal, -the method comprising the steps of: selecting, from the succession of samples, those having any one of a plurality of predetermined signal levels, accumulating a count of the samples of each predetermined signal level, and determining whether the accumulated counts conform to a predetermined distribution which is characteristic of noise. Further, according to the present invention there is provided apparatus to detect noise in a succession of samples of an electrical signal, the apparatus comprising a plurality of comparator gates to select from the succession of samples those having predetermined signal levels corresponding respectively to the comparator gates, means to accumulate a count of the samples selected by each gate and means to determine whether the accumulated counts conform to a predetermined distribution which is characteristic of noise. The invention will now be described, by way of example, with reference to the accompanying drawings in which: Figure 1 shows a block diagram of a noise level detector in accordance with the present invention and. Figures 2A to 2C are explanatory diagrams of probability density functions. Referring to Figure 1 , there is shown a noise level detector which receives at a terminal 10 an input signal A consisting of a television video signal having a succession of frames of interlaced fields. The input signal at the input terminal 10 is supplied to a frame delay 11. The input to the frame delay and the output B from the frame delay are applied to a difference circuit 12 which generates a signal representing the difference C between each frame of the television signal and the succeeding frame. The absolute value of the difference signal at the output from the difference circuit 12 is obtained by means of a detecting circuit 13. The absolute frame difference signal D is spatially filtered in a low-pass filter circuit 14 by adding three consecutive horizontal samples of the difference signal. The filter circuit 14 has a gain of three to amplify the filtered difference signal E. Other more complex filters could be used but it has been found in practice that they add little to the functionality of the noise detection. From the low-pass filter 14, a sampling circuit 15 derives a succession of samples F of the filtered difference signal. The number of samples is limited to reduce the complexity of the subsequent processing stages because it is the statistical properties of the signal that are important and not the instantaneous value. The sampled signal F is applied to a set of four comparator gates 16 each of which selects samples which have a signal level corresponding to a predetermined reference value F associated with the gate. Thus the first of the gates 16 has a reference value F1 , the second gate 16 has a reference value F2, the third gate 16 has a reference value F3 and the fourth gate 16 has a reference value F4. The samples selected by the gates 16 are passed to be counted by accumulators 17 associated respectively with the gates 16. If a pixel of the sampled signal F is equal in signal value to one of the reference values F1 to F4 of the gates 16, it is passed by the relevant gate to be added to the count in the respective accumulator 17 so that each count is incremented by one for each sample selected for that count. A synchronising signal detector 18 detects the frame synchronising signal in the input signal A and generates therefrom a reset signal to reset each accumulator 17 after each frame of the video signal. Thus at the end of each frame, the accumulators 17 contain counts of the samples of the signal F and these counts constitute a representation of the probability density function (PDF) of the output of the filtered signal E. The count values are read into a microprocessor 19 and reset in the vertical blanking interval ready for the analysis in the next frame. The microprocessor 19 implements a noise level algorithm to detect the presence of noise. The noise detection algorithm is based on evaluating the probability density function (PDF) of the output of the filtered signal E. Figures 2A, 2B and 2C show the PDFs of signal E for various types of video of signals with and without noise. It can be seen that Gaussian noise, in particular, leads to a characteristic local maximum in the PDF at low levels. The proposed algorithm is based on the principle of first establishing whether a local maximum exists in the PDF at relatively low levels and then determining the exact level of the local maximum which is proportional to the level of noise. Note that it is the position of the local maximum, not the magnitude, which gives an indication of the noise floor. The exact implementation of the noise algorithm depends on the number of comparators and counters used in the hardware and which can be 3 or more but can be varied from case to case. The principle, however, can be described on the basis of just four values. The four values are spread evenly over the dynamic range of the detector and, as such, represent a small subset of a discrete approximation of the PDF of signal F. From these samples an approximation of the continuous PDF is reconstructed by fitting, for example, a third-order polynomial to the data points as shown in equation (1 ) although the polynomial could be a second or higher order polynomial depending on the number of comparators and counters. PDF(x) = a +bx = cx^ + dx (1 ) Other approximations are possible but are not considered further here. Using four supporting values x 0 .. x 3 the coefficients of equation (1 ) can. be derived. As an example, the coefficients are derived for a spacing of Δ = 8, ie x 0 = 0, x, = 8, x 2 = 16 and x 3 = 24, as follows: a = PDF (0) (2) b = (PDF(24)-4.5PDF(16)+9PDF(8)-5.5PDF(0))/24 (3) c = (-PDF(24)+4PDF(16)-5PDF(8)+2PDF(0))/128 (4) d = (PDF(24)-3PDF(16)+3PDF(8)-PDF(0))/3072 (5) Having derived an approximate reconstruction of the PDF of signal ABC in the microprocessor, the noise level determination proceeds as follows. If there is no distinct local maximum discernible then the PDF is classified as signal with insignificant noise. A distinct local maximum in this sense is present if and only if the following two conditions are met. PDF(x 1 ) > c 1 PDF (x 0 ) (6) AND PDF ( x 2 ) > c 2 PDF (x 3 ) where c, and c 2 are constants c 1 = c 2 ≡ 2 In other words, if both PDF (x-,) and PDF (x 2 ) are greater by a certain margin than the PDF at the boundaries of the dynamic range then the noise level can be determined from the local maximum of the PDF polynomial approximation. This is done by solving the quadratic equation PDF' (x) = b + 2cx = 3dx 2 = 0 (8) which will have at least one solution in the range x 0 < x< x 3 by virtue of conditions (6) and (7). If both solutions are in the range x 0 < x < x 3 then the value with the larger value of PDF (x) is chosen. Therefore, equation (8) gives a measure of the noise level in the form N = -c/(3d) ± ((c/(3d)) 1/2 (9) Note that a solution of equation (9) within the dynamic range by itself is a necessary but not sufficient condition for the classification of PDF (x) as a signal with added noise. A method for the determination of noise levels in video sequences has been described. Simulations have shown that the algorithm is robust in so far as it is able to distinguish between low-level detail and additive Gaussian noise. There are many examples of signal processing applications where knowledge of the amount of noise which is present in image sequences is important. One of the more obvious applications of this method is the adaptive control of a noise filter which removes the detected noise. In this application it is particularly important that the algorithm can distinguish picture detail from noise, otherwise the application of the noise filter could lead to picture artefacts.

Claims: Claims 1. A method of detecting noise in a succession of samples of an electrical signal, the method comprising the steps of: selecting, from the succession of samples, those having any one of a plurality of predetermined -signal levels, accumulating a count of the samples of each predetermined signal level, and determining whether the accumulated counts conform to a predetermined distribution which is characteristic of noise. 2. A method according to claim 1 , wherein the step of determining whether the accumulated counts conform to a predetermined distribution, consists of determining whether the distribution exhibits a maximum within a predetermined range of signal values. 3. A method as claimed in claim 1 or 2, including the further step of deriving the said electrical signal by taking the difference of a video signal with a delayed version of itself. 4. A method as claimed in claim 3, wherein the step of deriving the said electrical signal consists of taking the absolute difference of a video signal with a version of itself which has been delayed by one frame. 5. A method as claimed in claim 3 or 4, including filtering the said difference signal by means of a low pass filter and sampling by means of a sampling circuit to derive the said succession of samples. 6. A method according to any one of the preceding claims wherein the plurality of predetermined signal levels are 3 or more in number. 7. A method as claimed in any one of the preceding claims wherein the step of determining whether the accumulated counts conform to a predetermined distribution consists of determining whether the predetermined distribution is representative of a second or higher order polynomial. 8. Apparatus to detect noise in a succession of samples of an electrical signal, the apparatus comprising a plurality of comparator gates to select from the succession of samples those having predetermined signal levels corresponding respectively to the comparator gates, means to accumulate a count of the samples selected by each gate and means to determine whether the accumulated counts conform to a predetermined distribution which is characteristic of noise. 9. Apparatus according to claim 8, wherein the means to determine whether the accumulated counts conform to a predetermined distribution consist of means for determining whether the distribution exhibits a maximum within a predetermined range of signal values. 10. Apparatus according to claim 8 or 9, further including difference means to derive the electrical signal from an input video signal by taking the difference of the video signal with a delayed version of itself. 11. Apparatus as claimed in claim 10, wherein the difference means is adapted to derive the absolute difference of a video signal with a version of itself which has been delayed by one frame. 12. Apparatus as claimed in claim 10 or 11 , wherein a low pass filter is provided to filter the difference signal and sampling means sample the filtered signal to derive the said succession of samples therefrom. 13. Apparatus as claimed in any one of claims 8 to 12, wherein the said comparator gates are 3 or more in number. 14. Apparatus as claimed in claim 11 , wherein the means to determine whether the accumulated counts conform to a predetermined distribution are effective to determine whether the predetermined distribution is representative of a second or higher order polynomial.

Inventors: BOCK ALOIS MARTIN, BOCK, ALOIS, MARTIN
Applicants: BOCK ALOIS MARTIN, NDS LTD, BOCK, ALOIS, MARTIN, NDS LIMITED
Application year: 1997
Main classification: H04N17
IPC codes: H04N17