Display standards in common use for the display of computer or television images on high resolution displays commonly assume that the output image will be displayed on an output device having a high refresh rate. A high refresh rate is normally required so the viewer does not observe flicker, stilted motion or other visual artifacts that one might observe if a lower refresh rate were used. It is difficult to drive some displays at a high refresh rate. An apparatus is disclosed for detecting areas of an input image that are changing over time, even where the input image may be subject to some degree of noise. A priority value is determined for providing a measure of this change for use with a system to determine which areas of the image may need changing as a matter of high priority.

Current U.S. Class:	348/699; 348/700; 348/701
Intern'l Class:	H04N 007/18
Field of Search:	348/154,155,402,407,413,416,699,700,701

                  TABLE 1
    ______________________________________
    2 to 1 Arbiter Truth Table
    A       B     Z         Do   a       b   V
    ______________________________________
    0       0     0         X    0       0   0
    0       1     0         Db   0       0   1
    1       0     0         Da   0       0   1
    1       1     0         Da   0       1   1
    0       0     1         X    0       0   0
    0       1     1         X    0       1   0
    1       0     1         X    1       0   0
    1       1     1         X    1       1   0
    ______________________________________

    ______________________________________
    Appendix A
    ______________________________________
    {Using a pseudo random number generator precalculate the
    multiplication patterns and store them in the array `patterns`.
    Patterns are strings of weights. The weight values are -1, 1 and
    `sums` is an array of three elements, one for each of the
    red, green and blue average signature units
    `pixel` is a multidimensional array representing the input
    image. This array stores a red, green and blue component of a
    pixel}
    read patterns
    for each L (line in image)
    begin
    for each S (segments in a line)
    begin
    initialise sums
    for each P (pixels in line)
    begin
             for each C (colour)
             begin
                sum[C] = sum[C] +
                  pattern[P] [C}*pixel[C] [P] [S] [L]
             end (colour)
    end (pixels)
    store sums
    end (segments)
    end (lines)
    ______________________________________


______________________________________
    Appendix B
    ______________________________________
    {mask.sub.-- patterns are strings of `0`, `1` & `2`
    `0` means don't sample the edge
    `1` means sample the positive edge
    `2` means sample the negative edge
    These values are used as a mask to determine which bin
    should sample the positive edge, and which bin should sample the
    negative edge. For example, for a system with nine bins then, for
    a current edge, the pattern 100000002 would mean that the first
    bin samples the value of the positive edge and the ninth bin
    samples the value of the last edge.
    There is a mask.sub.-- patterns entry for each bin in each colour
    mask.sub.-- patterns are different on successive lines but may repeat
    after n (>3) lines}
    read mask.sub.-- patterns
    for each L (lines in image)
    begin
    for each S (segments in line)
    begin
    initialize bins
    for each P (pixels in segment)
    begin
              for each C (colour)
            begin
    {for each set of pixels, determine the difference between
    the two pixels and create two edges from this information}
              diff = pixel[P] [C] - pixel[P+1] [C]
              if diff>0 then
               slope.sub.-- height[P] [C] [pos] = diff
               slope.sub.-- height[P] [C] [neg] = 0
              else
               slope.sub.-- height[P] [C] [pos] = 0
               slope.sub.-- height[P] [C] [neg] = -diff
             end (colour)
    {assign the edge to its corresponding bin}
    for each B (bins)
    begin
    if mask.sub.-- patterns[L] [P] [C] [B] = 1 then
               bin[B] [S] =
               max(bin[B] [S],step.sub.-- height[P] [C] [pos])
    else
               if mask.sub.-- patterns[L] [P] [C] [B] = 2 then
               bin[B] [S] =
               max(bin[B] [S],step.sub.-- height[P] [C] [neg])
    end (pixels)
    store bins
              end (segments)
    end (lines)
    ______________________________________


______________________________________
    Appendix C
    ______________________________________
    for each L (lines in image)
    begin
    max.sub.-- edge = max.sub.-- ave = 0
    for each S (current signature in line)
    begin
    fetch reference signature
    diff = abs(current signature - reference signature)
    if (current signature is average type) then
             max.sub.-- ave = max(max.sub.-- ave, diff)
    else
    max.sub.-- edge = max(max.sub.-- ave, diff)
    endif
    end (signature)
    dispatch max.sub.-- ave, max.sub.-- edge
    end (lines)
    ______________________________________