Analog cable television systems are susceptible to additive noise impairments, most commonly, thermal noise, composite second order beats and composite triple beats. Digital cable broadcasting has been introduced to solve these problems. However, during the period of migrating from analog systems to full digital systems, alternative techniques are required to bridge the gap of image qualities between the two systems. Video noise reduction is one of the proposed solutions. Among recent inventions, digital image processing related techniques are widely used in reducing video noise. The basic idea underlying most of existing algorithms, and this invention, is to estimate the original value of an impaired signal with redundant data in the neighborhood. As it is well understood that correlation may adhere spatially and temporally, in order to maximize the effectiveness of an algorithm, most systems use both spatial and temporal correlated pixels for the calculation. However, with a limited number of frames of noisy video pictures, it is extremely difficult to conclude whether a difference between contiguous pixels is caused by motion, detail variation or noise, especially when the difference is subtle. Techniques providing detail preservation for spatial filtering and motion preservation for temporal filtering are crucial to the effectiveness of an algorithm. But now, unfortunately, there is no such a practical method that could reduce noise while introducing no artifacts. Available techniques compare themselves to each other with the concept of minimum visible artifacts, which is rather a subjective measure. Combined with signal-to-noise ratio measurement, real-time subjective evaluation is better accepted by industries than instrument measurement results alone as subjective methods are more directly related to temporal characteristics of an algorithm and also the reactions of human eye perception system.
In the past decade, almost all the related systems developed have been focusing on recursive type algorithms which have a direct benefit of saving expensive storage memory. However, the price one has to pay for the memory saving is reduced visual fidelity of the processed image, even though the signal-to-noise ratio measured might show equivalent noise reduction efficiency.
A conventional inter-frame motion detection algorithm, as is well known, assumes a pixel is in a motion area if the difference between the pixel under examination and its corresponding pixel in the history frame is greater than a threshold. It is obvious that the lower the threshold, the less the artifacts are likely to be introduced. However, the lower the threshold, the more chance that a high intensity noise would cause the motion detection logic false alarming. Michael et al. in U.S. Pat. No. 4,240,106 described two motion detectors which overcome some of the deficiencies of the above described basic motion detector. The Michael et al. detector employs an array of pixel differences disposed about the pixel currently being examined for motion. In the first system the respective picture point differences are independently compared to a threshold value to generate bi-level signals corresponding to a majority logic gate which produces a motion signal if the majority of the pixel differences in the array exceed the threshold. In the second system, the pixel differences, of the array of pixels centered about the pixel under examination, are integrated to generate an average difference over the area. The average difference is then compared with a threshold. The object here is to differentiate between noise and movement. It is assumed that the larger the number of picture points integrated, the more likely the noise will average to zero whereas movement remains unattended.
Roeder et al. in U.S. Pat. No. 4,661,853 divide the difference matrix formed by target pixel and its surrounding pixels into several sub-matrices, hence improves the property of detail preservation. However, compared with the Michael et al. detector, Roeder detector is less robust in heavy noise situation as less pixels are involved in the averaging.
All methods cited above try to improve their noise immunity, in the motion area or on the boundary of the motion area, by averaging the differences over certain spatial areas. C. P. Sandbank in his book "Digital Television" gives a complete theoretical analysis on algorithms in this category. As a conclusion draw from this book, compromise has to be made between detail motion sensitivity and noise immunity.
Lee in U.S. Pat. No. 5,166,788 described a motion spread technique to protect motion-still transition area. The invention mainly targets adaptive comb filtering applications. A soft switch between a temporal filter and a paralleled spatial filter is used around the motion boundaries. For noise reduction purpose, a disadvantage of this algorithm is that the spatial filter is virtually idle on the still areas in a video image.