as discussed in the background to invention , fig1 shows an image 10 on a white background 12 . the edge of the shape 10 is referred to as a ‘ hard edge ’ because the color intensity values drop from maximum intensity to zero ( white ) instantly . as there are no transition colors in the image , the histogram shown in fig2 only has data in levels 0 and 255 ( 8 bit color values ). when the image of fig1 is jpeg compressed for efficient transmission , and subsequently decompressed at the output device , the edge of the shape 10 is surrounded grey pixel noise 14 . the noise 14 is sometimes referred to as mosquito noise as it is most visible as a cloud of dark dots on the light side of a hard edge such as text or line art . the loss of detail in the data during compression can shift pixels color values away from their input values . these shifts are likely to be small so in a contone image with smoother tone gradients , the noise is high frequency and invisible to the eye . however , the noise can become visible around hard edges where input data at level ‘ 0 ’ shifts to nearby grey levels from decompression errors . this noise 14 is shown in the histogram of fig4 . input data at level ‘ 255 ’ also shifts but is less visible amongst the predominantly level 255 pixels . this noise is shown at 18 on the histogram of fig4 . fig5 shows the settings for a histogram expansion to remedy the majority of the noise . ordinary workers in this field will appreciate that a histogram expansion rescales the input data by reducing the number of levels in the bandwidth . in fig5 , levels 1 to 16 are mapped down to level 0 and levels 240 to 254 are mapped up to level 255 . then the intermediate levels are resealed , or widened , to encompass the greys between the extremes . the resulting histogram has fewer levels ( 224 ), with more contrast between adjacent levels . ordinarily , the increased color contrast is the primary purpose of histogram expansion , but in this case , the expansion has corrected most of the noise 14 as shown in fig6 . there is still some ‘ ringing ’ on either side of the edge between the shape 10 and the white background 12 , and this could be corrected with a more aggressive histogram expansion , but in contone images , a harsh reduction in levels leads to visible contouring in areas of gradual tone variation . ordinary workers will appreciate that each system will have an optimum compromise between clipping all the noise and degrading the number of color levels . it will also be appreciated that the buffers need not be the same size or symmetrically positioned in the signal bandwidth . it should also be noted that the buffers need not be at the extremes of the bandwidth . for example , if the signal is image data in a ycc - style color space ( luminance , chroma red and chroma blue ), the important data in the chroma channels is the neutral level in the middle of the bandwidth ( level 128 in 8 bit color values ). the important data in the luminance channel is at the extremes . the image of fig1 only has data in levels 0 and 255 , whereas most images have a much broader distribution of the data — usually across the full bandwidth . fig7 is a schematic representation of the histogram corresponding to a contone image such as a photograph . the image has some pixels at all levels of the 0 to 255 bandwidth . for storage and transmission , the image is typically jpeg compressed and then decompressed at the output device . depending the quality of jpeg compression selected , the number of pixels in the output levels should be roughly the same as the corresponding input levels . any errors that do occur , most likely put the pixel into a nearby level and this happens at high frequency so the noise is imperceptible . the exception to this is the hard edged components of the image such as text on a white background or line art . the eye does tend to register the noise generated in the 8 × 8 pixel blocks that span or contain such edges . in these areas , all pixels that were level 0 in the input image should be preserved as level 0 pixels in the output image in order to avoid the visible ringing . likewise level 255 pixels in the input image should be kept at that level in the output image although this is usually a less visible artifact . the quality of the jpeg compression is known , and so the section on the bandwidth in which the majority of noise induced in the level 0 pixels 12 is also known . these buffer sections 20 and 22 of the bandwidth are chosen as levels 1 - 16 and levels 240 - 254 . for the purposes of this example , the vast majority of noise induced by decompressing levels 0 and 255 will appear in these buffer sections . fig8 shows the image data moved out of the buffer section 20 and 22 by rescaling the input data 24 of levels 1 - 254 into levels 17 to 239 . rescaling is done using a simple algorithm such as equation 1 below : l r = l 1 + l i . ( l h − l 1 )/(( l max − 1 )− l min ) eq . 1 l r is the resealed level that the pixels in the input level are mapped to ; l 1 is the threshold of the buffer section at the lower end of the bandwidth ; l h is the threshold of the buffer section at the upper end of the bandwidth ; l i is the input level being resealed ; l max is the upper extremity of the bandwidth ; and , l min is the lower extremity of the bandwidth . in the example shown in fig7 - 10 , l max = 255 , l min = 0 , l 1 = 16 and l h = 239 . so eq . 1 becomes : l r can be rounded to the nearest integer or left as a floating point value to more accurately invert the rescaling process when the input data 24 is distributed back across the full bandwidth . fig8 shows levels 1 to 254 resealed to move the input data out of the buffer sections 20 and 22 . the data 12 and 10 in levels 0 and 255 respectively , is not rescaled . this is the data that is being ‘ quarantined ’ from the rest of the data 24 by the buffer sections 20 and 22 . fig9 shows the histogram corresponding to the image data after it has been jpeg compressed , transmitted to an output device and subsequently decompressed . the data 16 and 18 in the buffer sections 20 and 22 must be noise from levels 0 or 10 ( or from the rest of the image data 24 , but as explained above , this noise is generally invisible to the eye ). therefore , mapping the data in buffer 20 back to level 0 and mapping the data in buffer 22 up to level 255 will correct any noise that would otherwise turn up in the white background or the full color side of a hard edge component in the image . some noise in the buffers is from the rest of the data 24 . these pixels are also mapped to 0 or 255 and so end up slightly more erroneous in the output image , but the eye will not be able to see this . once the noise in levels 0 and 255 has been corrected , the rest of the data 24 can be redistributed back into the now vacant buffer sections 20 and 22 , as shown in fig1 . this is done by simply reversing equation 1 . because the bandwidth was resealed to a smaller number of levels , expanding the data back out to the full bandwidth will mean the loss of some levels due to rounding . this means that the resulting contone image will be quantized slightly more coarsely . however , sacrificing 32 levels still leaves 224 which is ample for photographic quality image reproduction by the output device . indeed , 90 to 100 levels is often deemed sufficient . it should also be noted that the manipulation of the image data can be achieved in a computationally efficient way by incorporating it into existing data processing steps . the rescaling of the signal to move data out of the buffer sections can be done when the image data is color space converted from rgb to cmy ( k ). furthermore , the re - distribution of data back across the full bandwidth can be done by adjusting the threshold values used in the dither matrix during halftoning . the applicant &# 39 ; s co - pending u . s . ser . no . 11 / 482 , 980 incorporated herein by reference describes how the histogram can be expanded ( or contracted ) using the dither matrix . in light of this , the computational cost for preserving the data at one or more levels in the bandwidth is relatively little , yet the tangible image quality improvement is significant . the threshold values in the compressed range in the secondary matrix are determined using equation 2 : t new = l 1 + t old . ( l h − l 1 )/ 256 eq . 2 t new is the compressed threshold values in the secondary dither matrix ; and , t old is the threshold value in the primary dither matrix . the adjusted dither matrix will not affect data at the extreme levels of the bandwidth as level 0 will still be below the lowest threshold value and level 255 will still be above the highest threshold level . only data in the levels between the buffer sections will be ‘ expanded ’ by the adjusted dither matrix . as discussed above , the noise in the white background of a hard edge is more visible than the noise in the full color area along the edge . this is particularly true if the output device is a printer . in light of this , only the data in level 0 can be preserved for a significant reduction in noise . this reduces the rescaling of the remaining signal so that when it is expanded back to the full bandwidth , less levels are lost and the contone image is not as coarse . in other embodiments , the input signal is rescaled in a manner different to that the technique set out in equation 1 . fig1 shows the hard edged rescaling used in the above example . all the data in the buffer sections is shifted to the levels between 16 and 240 . however , the output image may be improved by rescaling the image with a non - linear function as shown in fig1 where most of the data is removed from the buffer sections but not all . of course , equations 1 and 2 would not apply to this method of rescaling . the invention has been described herein by way of example only . skilled workers in this field will readily recognize many variations and modification that do not depart from the spirit and scope of the broad inventive concept .