as set forth in the above referenced copending application , a modification to the visual error diffusion algorithm that corrects for these undesirable artifacts has been disclosed in which a visual filter function is applied to both the input and output images during the process of determining the output value for the current pixel . the error that is propagated to nearby continuous - tone input pixels that have not been processed yet is calculated by the difference between the visually perceived input value and the visually perceived output value . this invention has the advantage that it minimizes the appearance of worm artifacts in the output image while eliminating the edge artifacts associated with the prior art visual error diffusion algorithm . a block diagram of the improved visual error diffusion algorithm is shown in fig8 and a sample output image is shown in fig1 . one drawback of the fig8 method is that in order to restore the desirable appearance of sharpness to the image , a sharpening pre - filter 68 is required . an example sharpening pre - filter , shown in fig9 was used in creating the image of fig1 . thus , the edges in fig1 appear as sharp as the edges in fig7 which was processed with the visual error diffusion method of sullivan , but with the added advantage of being isotropic in nature . while this improvement produces a higher quality output than the previous methods , it does introduce undesirable noise 21a - b into certain areas of the image that are high in contrast and spatial frequency , such as the vertical and horizontal bars near the center right of the sample image of fig1 . in the present invention , shown schematically in fig1 , a pixel located in row i , column j of the input image is denoted by y ( i , j ). an activity detector 80 uses neighboring pixels in the vicinity of y ( i , j ) to determine an activity signal . the activity detector 80 may take many forms , including a local range detector , local variance estimator , convolution filter , or edge detection operator such as a sobel or prewitt edge detection filter . in one version , the activity detector 80 uses a local range detector to compute the activity signal as the difference between the maximum and minimum input pixel values of neighboring pixels in the vicinity of y ( i , j ). the neighboring pixels may include the current pixel , adjacent pixels , and / or other nearby pixels . another activity detector 80 , in accordance with the invention , uses a local variance estimator to compute the activity signal as the statistical variance of the input pixel values of neighboring pixels in the vicinity of y ( i , j ). still another activity detector 80 uses a convolution filter to compute the activity signal as the convolution of the convolution filter and the input pixel values of neighboring pixels in the vicinity of y ( i , j ). an activity detector using an edge detection operator computes the activity signal as the sum of convolutions of the input pixels of neighboring pixels in the vicinity of y ( i , j ) with the edge detection filters . the value of the activity signal is used as an index to an activity function 82 which computes a weighting vector q ={ q 0 , q 1 , . . . , q k , . . . , q n - 1 }. in the preferred embodiment of this invention , the activity function 82 is implemented as a lookup table that is indexed by the activity signal . the input image is also processed through an input filter processor 81 , which digitally filters the input image in the vicinity of y ( i , j ) using filtered signal generators 81a and a bank of n separate filters f 0 ( i , j ) through f n - 1 ( i , j ) as shown in fig1 . also in fig1 , the weighting vector q is used to weight the n filtered versions of the input image using multipliers 81b , which are then added together using an adder 81c to produce a weighted filtered input signal z ( i , j ). the filtered input values only need to be computed for cases where the weights q k are non - zero . an example of an activity function 82 that can be used to switch between two input filters f 0 ( i , j ) and f 1 ( i , j ) is shown in fig1 , where | e | is the value of the activity signal . in this particular example , the value of the activity signal ranges from 0 to 255 . for values of the activity signal between 0 and 10 , q ={ 0 , 1 }, and the input filter f 1 ( i , j ) will be used . for values of the activity signal between 10 and 255 , q ={ 1 , 0 }, and the input filter f 0 ( i , j ) will be used . in this way , the method of the present invention adapts the image processing based on the value of the activity signal . this feature can be generalized so that a different input filter is selected for each of n types of local scene content in the input image , as discriminated by the activity detector . additionally , the use of the filters f 0 ( i , j ) through f n - 1 ( i , j ) can be blended in regions of the image where it is unclear exactly what type of scene content is present . an example of an activity function 82 which will accomplish this is shown in fig1 . at the output of the input filter processor 81 , it is desired that the average value of z ( i , j .) be equal to the average value of y ( i , j ) for uniform regions of the input image . one way of satisfying this average preserving relationship is to use filters f 0 ( i , j ) through f n - 1 ( i , j ) that are normalized ( i . e ., that satisfy the relationship σ i σ j f ( i , j )= 1 ), and impose the constraint that q satisfies the relationship σ iqi = 1 . after the weighted filtered input signal z ( i , j ) is computed , the errors made by processing previous pixels are weighted by the error weights w ( i , j ) shown in fig2 and by the weighting vector q using a weighted error generator 83 ( fig1 ). the details of the weighted error generator 83 are shown in fig1 , in which error values stored in n separate error buffers b 0 ( i , j ) through b n - 1 ( i , j ), corresponding to the n types of local scene content , are individually weighted by w ( i , j ) using filtered error generators 83a , then weighted by the the weighting vector q using multipliers 83b , and added together using an adder 83c to produce an error signal e ( i , j ). in this way , the errors generated in areas of the image that contain one type of scene content will not propogate to an area of the image that contains a different type of scene content . this type of separate error buffering is desirable to avoid artifacts that can be generated when the image contains a sharp boundary between areas of different scene content , such as the boundary between the crosshair patches and the horizontal and vertical stripe patches near the center right of fig1 . returning to fig1 , the error signal e ( i , j ) is added to the weighted input signal z ( i , j ) by an adder 88 to compute the desired signal d ( i , j ). an output filter processor 89 computes a set of m weighted filtered output values p 0 through p m - 1 . this is done by digitally filtering the previously computed output values o ( i , j ) together with each of the m possible output levels 85 for the current pixel ( l 0 through l m - 1 ) using filtered signal generators 89a and the bank of n filters g 0 ( i , j ) through g n - 1 ( i , j ) as shown in fig1 to compute filtered output values . for each of the m possible output levels , the filtered output values corresponding to the n filters are weighted by the weighting vector q using multipliers 89b , and added by an adder 89c to generate the weighted filtered output values , as shown in fig1 . the filtered output values only need to be computed for cases where the weights q k are non - zero . returning again to fig1 , once the weighted filtered output values p 0 through p m - 1 have been computed , a selector 86 chooses the output level l 0 through l m - 1 according to an error criterion . one such error criterion is to minimize the difference between the desired signal d ( i , j ) and the weighted filtered output values p 0 through p m - 1 . once a selection of an output level has been made , an output filter processor 87 digitally filters the output signal o ( i , j ) using filtered signal generators 87a and the bank of n filters g 0 ( i , j ) through g n - 1 ( i , j ), weights the results by the weighting vector q using multipliers 87b , and adds the weighted values together using an adder 87c as shown in fig1 . an error signal generator 90 ( fig1 ) then generates an error signal by taking the difference between the value generated by the output filter processor 87 and the desired signal d ( i , j ). the error signal is then distributed into n error buffers b 0 through b n - 1 by an error distributor 84 which weights the error signal by the weighting vector q using multipliers 84a as shown in fig1 . the values stored in the n error buffers b 0 through b n - 1 are then weighted by the weighted error generator 83 and added using the adder 88 to weighted filtered input pixels which have not yet been processed , as described earlier . a sharpening pre - filter can be applied to the input image in order to restore the desirable edge sharpness . if the sharpening is implemented by a device which performs a convolution , then it is possible to incorporate the sharpening pre - filter directly into the input filtering operation 81 by convolving the sharpening pre - filter with the filters f 0 ( i , j ) through f n - 1 ( i , j ) to arrive at a new set of input filters . it may be desirable to only apply the sharpening filter to portions of the image which contain large amounts of activity . this can be accomplished by only incorporating the sharpening pre - filter into a subset of the filters f 0 ( i , j ) through f n - 1 ( i , j ) which correspond to large values of the activity signal . a sample image generated using the method of the present invention is shown in fig1 . the activity function shown in fig1 was used to switch between image regions having high and low activity signal values . the activity detector 80 used in this example was a local range detector . in regions of low activity , the input filter f 1 ( i , j ) used was a delta function δ ( i , j ), where δ ( i , j ) is defined as 1 for i = j = 0 and 0 otherwise . as a result , the input filter operation reverts to a null operation and z ( i , j ) is equal to y ( i , j ). the output filter used for low activity regions g 1 ( i , j ) was set equal to the causal visual filter v ( i , j ) shown in fig6 . in the high activity regions , the input filter f 0 ( i , j ) used was the sharpening filter 68 shown in fig9 . the output filter g 0 ( i , j ) used was the delta function δ ( i , j ). for values of the activity signal between 0 and 10 , q ={ 0 , 1 }, and the visual error diffusion method of fig4 is used . alternately , for values of the activity signal between 10 and 255 , q ={ 1 , 0 }, and the conventional error diffusion of fig1 with a pre - sharpening step is used . in this way , the method of the present invention adapts the processing based on the value of the activity signal . another embodiment of the invention employs a single buffer to store the error signals generated by the error signal generator 90 , as shown in fig2 . comparing this with the method shown in fig1 , it can be seen that the n separate error buffers b 0 through b n - 1 have been eliminated and the error signal feeds directly into a weighted error generator 91 . the weighted error generator 91 then computes a weighted error signal e ( i , j ) using the weighting vector q and the error signal from the error signal generator 90 . for color images , the method described above can be applied to each channel of the image as shown in fig2 . for example , if an image consists of red , green , and blue pixels , the improved adaptive error diffusion method can be applied via block 70 to the red pixels , and again to the green pixels and the blue pixels by block 71 and 72 , respectively . similarly , the improved adaptive error diffusion method can be applied to each channel of a 4 - color cyan , magenta , yellow and black image . it may be desirable to use different input filters , output filters , activity lookup tables , error weights , activity functions , and / or sharpening filters can be used for each color channel . another method of processing color images according to the present invention is to process all of the color channels simultaneously , as shown in fig2 . it can be seen that the logic of fig2 is the same as the logic of fig1 , with the exception that the signals that are communicated between logical blocks are now vector signals , as opposed to scalar signals discussed in the method of fig1 . in the method of fig2 , the input continuous - tone pixel values for row i column j of the color channels are combined into a color vector y ( i , j ). a vector activity detector 100 determines an activity signal from y ( i , j ), and the activity function 82 determines a weighting vector q from the activity signal . the input color vector y ( i , j ) is then filtered by a vector input filter processor 101 , which generates a filtered input color vector z ( i , j ). a weighted color vector error e ( i , j ) is computed by a weighted color vector error generator 102 and added to z ( i , j ) by a vector adder 108 , resulting in a desired color vector d ( i , j ). a vector output filter processor 109 determines a set of weighted filtered output color vectors p 0 through p m - 1 by filtering previous output color vectors o ( i , j ) along with possible output color vectors 105 ( l 0 through l m - 1 ) using a set of output filters g 0 through g n - 1 and the weighting vector q . a selector 106 chooses an output color vector according to an error criterion . one such error criterion is the minimum vector distance between the desired color vector d ( i , j ) and the weighted filtered output color vectors p 0 through p m - 1 . once an output color vector has been chosen , a vector output filter processor 107 filters the output color vector o ( i , j ) using a set of output filters g 0 through g n - 1 and the weighting vector q . a color vector error signal generator 110 computes a color vector error signal as the vector difference between the output of the output filter processor 107 and the desired color vector d ( i , j ). the color vector error signal is then distributed into n vector error buffers b 0 through b n - 1 using a vector error distributor 103 which is responsive to the weighting vector q . the n vector error buffers b 0 through b n - 1 are then used by the weighted color vector error generator 102 to compute a weighted color vector error e ( i , j ) for use in processing future color vectors . the invention has been described in detail with particular reference to certain preferred embodiments thereof , but it will be understood that variations and modifications can be effected within the spirit and scope of the invention .