CROSS-REFERENCE TO RELATED APPLICATIONS  
       [0001]     This application claims the benefit under 35 U.S.C §119 (a) of Korean Patent Application No. 2004-41929, filed on Jun. 8, 2004, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety.  
       BACKGROUND OF THE INVENTION  
       [0002]     1. Field of the Invention  
         [0003]     The present general inventive concept relates to an apparatus and method to provide noise measurement in image signals. More particularly, the present general inventive concept relates to an apparatus and method of measuring noise in image signals according to spatial and temporal frequency components, thereby enhancing efficiency of removing the noise.  
         [0004]     2. Description of the Related Art  
         [0005]     When an image signal-processing device, such as televisions or video tape recorders, is supplied with image signals, it is often the case that a noise is entrained in the image signals. The noise in the image signals typically causes a reduction in the quality of images in video signals. To reduce the noise in the video signals, various noise measurement apparatuses have been developed. An efficiency of removing the noise depends on the accurate noise measurement.  
         [0006]      FIG. 1  is a view showing a conventional noise measurement apparatus. Referring to  FIG. 1 , a noise measurement apparatus comprises an SAD calculator  100 , an SAD comparator  102 , a first counter  104 , a comparator  106 , a second counter  108 , and a multiplier  110 .  
         [0007]     The SAD calculator  100  breaks an input image signal into a plurality of blocks (e.g., 175,000 blocks) each of which is configured by pixels, and calculates an SAD (Sum of Absolute Difference) with respect to each block.  
         [0008]     The SAD calculated by the SAD calculator  100  is transmitted to the SAD comparator  102 . The SAD comparator  102  determines whether the SAD transmitted from the SAD calculator  100  exists between a threshold A and a threshold B. If the SAD is determined to exist between the threshold A and the threshold B, the SAD comparator  102  transmits to the first counter  104  an existence-notifying signal (OK signal) by which a counted value of the first counter  104  is increased.  
         [0009]     The first counter  104  is reset by a picture frequency signal Fp once for a picture period. Alternatively, the first counter  104  may be reset once for another period, for example, a field period or multiple fields period. In this case, a proper reset signal has to be applied to the first counter  104 .  
         [0010]     The SAD calculator  100 , the SAD comparator  102 , and the first counter  104  receive a clock signal of a sample frequency Fs and are reset by the received Fs. A value counted by the first counter  104  is transmitted to the comparator  106 , and the comparator  106  compares the counted value with a predetermined value NE. The predetermined value NE is a preset integer that is experimentally obtained. It is preferable that NE=496, which corresponds to 0.28% of total numbers of the blocks. A result of comparing by the comparator  106  is transmitted to the second counter  108 .  
         [0011]     The second counter  108  increases and decreases its counted value according to the result obtained by the comparator  106 . If the value counted by the first counter  104  is larger than or equal to the NE, the second counter  108  decreases the counted value thereof. On the other hand, if the value counted by the first counter  104  is less than the NE, the second counter  108  increases its counted value. The second counter  108  is reset by the reset signal applied to the first counter  104 , i.e., the clock signal of the picture frequency signal Fp. The valued counted by the second counter  108  results in a noise measurement, a low threshold A of the SAD comparator  102 , and a high threshold value B which is obtained by the multiplier  110  as a result of multiplying the low threshold A by a value ‘f’.  
         [0012]     The value ‘f’ is preferably set to 1.5, and it may be set to a sum of the low threshold A and a fixed offset value. The high threshold B of the SAD comparator  102  depends on the counted value of the second counter  108 , and the low threshold A is set to a fixed value such as 0 or a predetermined positive integer.  
         [0013]      FIG. 2  is a view showing one example of the SAD calculator  100  of  FIG. 1 . Referring to  FIG. 2 , the SAD calculator  100  comprises delayers  200 ,  204 ,  208  and  210 , an absolute difference calculator  202  and adders  206 ,  212 , and  214 .  
         [0014]     Pixels of the input image signal are delayed by the delayer  200  as much as one period. At this time, the SAD is calculated by a difference between horizontally neighboring pixels. If the SAD is calculated by a difference between vertically neighboring pixels, the delayer  200  has to be embodied by a line delayer.  
         [0015]     The absolute difference calculator  202  calculates an absolute difference between an input value and an output value of the delayer  200 . The absolute difference calculated by the absolute difference calculator  202  is transmitted to the delayers  204 ,  208 , and  210  that are sequentially connected to one another.  
         [0016]     The adder  206  adds the absolute difference calculated by the absolute difference calculator  202  to the absolute difference firstly delayed by the delayer  204 . The adder  212  adds the absolute difference secondly delayed by the delayer  208  to the absolute difference thirdly delayed by the delayer  210 . The adder  214  obtains a sum of the value of the adder  206  and the value of the adder  212 . The sum obtained by the adder  214  becomes the SAD that is inputted to the SAD comparator  102 .  
         [0017]     However, when the conventional noise measurement apparatus measures a noise in image signals, the SAD is calculated with respect to a spatial area of the image signals. Therefore, the noise measurement cannot be implemented adaptively to characteristics of the image signals, and thus an error occurs. For example, when the entire image has no plane area, the error may occur in the noise measurement.  
       SUMMARY OF THE INVENTION  
       [0018]     In order to solve the above and/or other problems, the present general inventive concept provides a noise measurement apparatus which is capable of reducing an error when measuring a noise in an image signal, and a method thereof.  
         [0019]     The present general inventive concept also provides is to provide a noise measurement apparatus which is capable of reducing an error when measuring a noise in an image having no plane area.  
         [0020]     Additional aspects and advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.  
         [0021]     The foregoing and/or other aspects and advantages of the present general inventive concept are achieved by providing a noise measurement apparatus for an image signal comprising: a block average estimation part that breaks a picture of an incoming image signal into at least two blocks and calculates an average brightness value with respect to each block in a sequence; a spatial noise measurement part that calculates at least two first data, each being a sum of differences between the average brightness value transmitted from the block average estimation part and brightness values of respective constituent pixels of the block where the average brightness value is calculated from, and calculates a spatial noise based on the at least two first data; a temporal noise measurement part that calculates at least two second data that indicate a difference between a brightness value of each block of the picture and a brightness value of each block of a delayed picture, and calculates a temporal noise based on the at least two second data; and a noise calculation part that calculates a noise in the image signal based on the spatial noise and the temporal noise.  
         [0022]     The foregoing and/or other aspects of the present general inventive concept are also achieved by providing a noise measurement method of an image signal comprising: breaking a picture of an incoming image signal into at least two blocks and calculating an average brightness value with respect to each block in a sequence; calculating at least two first data, each being a sum of differences between the calculated average brightness value and brightness values of respective constituent pixels of the block where the average brightness value is calculated from, and calculating a spatial noise based on the at least two first data; calculating at least two second data that indicate a difference in a brightness value of each block of the picture and a brightness value of each block of a delayed picture, and calculating a temporal noise based on the at least two second data; and calculating a noise on the image signal based on the spatial noise and the temporal noise. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0023]     These and/or other aspects and advantages of the present general inventive concept will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:  
         [0024]      FIG. 1  is a view showing one example of a conventional noise measurement apparatus;  
         [0025]      FIG. 2  is a view showing one example of a SAD calculator of  FIG. 1 ;  
         [0026]      FIG. 3  is a view showing an image signal used in measuring a noise according to an embodiment of the present general inventive concept;  
         [0027]      FIG. 4  is a block diagrams showing a noise measurement apparatus according to an embodiment of the present general inventive concept;  
         [0028]      FIGS. 5A and 5B  are views showing an interlaced scan method and a progressive scan method to explain operations of the noise measurement apparatus of  FIG. 4 ;  
         [0029]      FIG. 6  is a view showing a picture broken into a plurality of blocks; and  
         [0030]      FIG. 7  is a view showing a noise measurement apparatus according to another embodiment of the present general inventive concept. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0031]     Reference will now be made in detail to the embodiments of the present general inventive concept, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. The embodiments are described below in order to explain the present general inventive concept while referring to the figures.  
         [0032]     The present general inventive concept describes a method of reducing an error of a noise measured by using both a spatial area and a temporal area of an image signal.  
         [0033]      FIG. 3  illustrates an image signal inputted to a noise measurement apparatus  302  according to the present general inventive concept. The noise measurement apparatus  302  is inputted with a current image signal and a one-picture-delayed image signal which is obtained by a delayer  300 . Although  FIG. 3  depicts the image signal is delayed by the delayer  300 , this should not be considered as limiting. That is, the noise measurement apparatus  302  may be inputted with a one-picture-delayed image signal which is obtained by a noise remover, a progressive scan converter or a picture velocity converter.  
         [0034]      FIG. 4  is a block diagram illustrating one example of a noise measurement apparatus  302   a  of the noise measurement apparatus  302  of  FIG. 3 , according to an embodiment of the present general inventive concept. The noise measurement apparatus  302   a  of  FIG. 4  comprises a spatial MAD (Mean Absolute Difference) estimation part  400 , a spatial MAD comparison part  402 , a spatial MAD storage part  404 , a spatial noise calculation part  406 , a block average estimation part  408 , a section counter  410 , a temporal MAD estimation part  412 , a temporal MAD comparison part  414 , a temporal MAD storage part  416 , a temporal noise calculation part  418 , and a noise calculation part  420 . Although  FIG. 4  depicts only particular components to explain an embodiment of the present general inventive concept, the noise measurement apparatus  302   a  may further comprise other components. The noise measurement apparatus  302   a  may be used in an image signal processing apparatus.  
         [0035]     A method of realizing a digital image is divided into an interlaced scan method and a progressive scan method according to a frame configuring method. According to the interlaced scan method as shown in  FIG. 5A , a frame is created by scanning two fields line by line and sequentially, and then combining the two fields. More specifically, one field (top field) is scanned with odd lines (illustrated in solid arrows) and the other field (bottom field) is scanned with even lines (illustrated by dotted arrows), and then, by combining the two fields, a frame is created. In contrast with the interlaced scan method, the progressive scan method as shown in  FIG. 5B  doubles scan lines, thus achieving a high density image and a high quality image, and scans one frame with image signals. According to the interlaced scan method, one field configures a picture of an image signal, and according to the progressive scan method, one frame configures a picture of an image signal.  
         [0036]      FIG. 6  illustrates one example of a picture broken into a plurality of blocks. Referring to  FIG. 6 , the picture is broken into M blocks in a horizontal axis direction and N blocks in a vertical axis direction. Accordingly, one picture is broken into M×N blocks. The M and N depend on a user&#39;s setting. The user increases the M and N for an accurate noise measurement and decreases the M and N for a reduction of calculation amounts.  
         [0037]     The block average estimation part  408  breaks an incoming current image signal (picture) into a predetermined number of blocks and calculates an average brightness value with respect to each block. The block average estimation part  408  breaks a frame or a field of the incoming current image signal into a predetermined number of blocks, each of which has a predetermined size. The predetermined number of blocks are illustrated in  FIG. 6 .  
         [0038]     One block contains m×n pixels, where m indicates a number of pixels existing in a horizontal direction and n indicates a number of pixels existing in a vertical direction. The block average estimation part  408  calculates an average brightness value of each block. That is, the block average estimation part  408  obtains a sum of brightness values of the pixels within each block and calculates the average brightness value of the sum of brightness values by dividing the sum of the brightness values by the total number of pixels m×n.  
         [0039]     Hereinafter, a spatial noise measurement unit  430  and a temporal noise measurement unit  432  will now be described.  
         [0040]     The block average estimation part  408  performs the above-described operation M×N times in a sequence, thereby estimating block averages with respect to one picture. The block averages estimated by the block average estimation part  408  is transmitted to the spatial MAD estimation part  400 , the section counter  410 , the spatial MAD storage part  404 , and the temporal MAD storage part  416 .  
         [0041]     The section counter  410  matches the block averages transmitted from the block average estimation part  408  with one of a plurality of sections which correspond to brightness ranges obtained by dividing brightness levels (0 through 255) by, for example, 8, and increases a counted value of the matched section by 1. It is assumed that the block averages estimated by the block average estimation part  408  are from 0 to 255 and the section counter  410  has 8 sections. Table 1 below shows the 8 sections matched with the block averages by the section counter  410 .  
                           TABLE 1                                       Section 1    0 to 31           Section 2   32 to 63           Section 3   64 to 95           Section 4    96 to 127           Section 5   128 to 159           Section 6   160 to 191           Section 7   192 to 223           Section 8   224 to 255                      
 
         [0042]     As described above, the section counter  410  matches the inputted block averages with one of the above sections, and then increases a counted value of the matched section by 1. Table 2 below shows one example of counted values stored in the section counter  410  with respect to the respective sections.  
                           TABLE 2                                       Section 1   0           Section 2   2           Section 3   3           Section 4   3           Section 5   3           Section 6   2           Section 7   1           Section 8   0                      
 
         [0043]     The spatial MAD estimation part  400  obtains a difference between the block average transmitted from the block average estimation part  408  and the brightness value of each pixel configuring the block. The spatial MAD estimation part  400  obtains a sum of the obtained differences and then calculates an average as a special MAD. The operation of the spatial MAD estimation part  400  is identical to that of the SAD calculator  100  of  FIG. 2 . However, the SAD calculator  100  outputs the sum of differences with respect to the pixels, whereas the spatial MAD estimation part  400  obtains the sum of the differences with respect to the pixels and then outputs the average of the sum. The spatial MAD obtained by the spatial MAD estimation part  400  is expressed by the following equation 1.  
               Spatial   ⁢           ⁢   MAD     =         ∑     i   =   0         m   ×   n     -   1       ⁢                        ⁢       block   ⁢           ⁢   average     -                 saturation   ⁢           ⁢   value   ⁢           ⁢   of   ⁢           ⁢   ith   ⁢           ⁢   pixel                    m   ×   n               [     Equation   ⁢           ⁢   1     ]             
 
         [0044]     The spatial MAD comparison part  402  compares the spatial MAD transmitted from the spatial MAD estimation part  400  with a spatial MAD transmitted from the spatial MAD storage part  404 . The spatial MAD comparison part  402  transmits a smaller spatial MAD to the spatial MAD storage part  404 .  
         [0045]     The spatial MAD storage part  404  receives the block averages from the block average estimation part  408 . The spatial MAD storage part  404  groups the block averages into 8 and stores them as shown in tables 1 and 2. The spatial MAD storage part  404  stores in each section the spatial MAD transmitted from the spatial MAD comparison part  402 . Table 3 below shows the spatial MADs stored in the spatial MAD storage part  404  by way of an example.  
                           TABLE 3                                       Section 1 (0 to 31)               Section 2 (32 to 63)   12           Section 3 (64 to 95)   24           Section 4 (96 to 127)   21           Section 5 (128 to 159)   5           Section 6 (160 to 191)   4           Section 7 (192 to 223)   7           Section 8 (224 to 255)                      
 
         [0046]     The spatial MAD storage part  404  transmits to the spatial MAD comparison part  402  the spatial MADs stored in correspondence to the block averages transmitted from the block average estimation part  408 . As one example, if the spatial MAD storage part  404  receives 72 from the block average estimation part  408 , it transmits 24 to the spatial MAD comparison part  402 . As described above, the spatial MAD comparison part  402  transmits to the spatial storage part  404  a small one of the received spatial MADs.  
         [0047]     When the spatial MAD storage part  404  performs an estimation, a comparison, and a storing with respect to one picture, it transmits the table 3 to the spatial noise calculation part  406 .  
         [0048]     The spatial noise calculation part  406  receives the table 3 from the spatial MAD storage part  404  and also receives the table 2 from the section counter  410 . The spatial noise calculation part  406  calculates an average with respect to the spatial MADs based on the table 3. The section having a counted value of 0 is not taken into consideration when the average with respect to the spatial MADs is calculated. That is, the sections 1 and 8 are not considered in calculating the average with respect to the spatial MADs. The spatial noise calculation part  406  calculates the average simply based on the table 3. However the spatial noise calculation part  406  takes a counted value in each section of table 2 into consideration when calculating the average. That is, the average may be calculated by varying a weight according to the counted value of each section. The spatial noise calculation part  406  calculates the average as a spatial noise with respect to the spatial MADs excluding the least spatial MAD and the greatest spatial MAD.  
         [0049]     The spatial noise calculation part  406  transmits the calculated spatial noise to the noise calculation part  420 .  
         [0050]     Hereinbelow, the temporal noise measurement unit  432  is described. An operation of calculating the temporal noise is similar to that of calculating the spatial noise.  
         [0051]     The temporal MAD estimation part  412  breaks a current image signal and a delayed image signal into a predetermined number of blocks, respectively. The temporal MAD estimation part  412  calculates a difference between a pixel of a block of the current image signal and a pixel of a block of the delayed image signal, wherein the block of the current image signal and the block of the delayed image signal correspond with each other. A temporal MAD with respect to a block consisting of m×n pixels is obtained by the following equation 2.  
               Temporal   ⁢           ⁢   MAD     =         ∑     i   =   0         m   ×   n     -   1       ⁢                saturation   ⁢           ⁢   value   ⁢           ⁢   of   ⁢           ⁢   ith   ⁢           ⁢   pixel                 of   ⁢           ⁢   current   ⁢           ⁢   image   ⁢           ⁢   signal     -               saturation   ⁢           ⁢   value   ⁢           ⁢   of   ⁢           ⁢   ith   ⁢           ⁢   pixel               of   ⁢           ⁢   delayed   ⁢           ⁢   image   ⁢           ⁢   signal                    m   ×   n               [     Equation   ⁢           ⁢   2     ]             
 
         [0052]     The temporal MAD comparison part  414  compares the temporal MAD transmitted from the temporal MAD estimation part  412  with a temporal MAD transmitted from the temporal MAD storage part  416 . The temporal MAD comparison part  414  transmits a smaller temporal MAD to the temporal MAD storage part  416 .  
         [0053]     The temporal MAD storage part  416  is inputted with the block averages from the block average estimation part  408 . The temporal MAD storage part  416  divides the block averages into 8 and stores them in each section as shown in tables 1 and 2. The temporal MAD storage part  416  stores in each section the temporal MADs transmitted from the temporal MAD comparison part  414 .  
         [0054]     The temporal MAD storage part  416  transmits to the temporal MAD comparison part  414  the temporal MADs stored in correspondence with the block averages transmitted from the block average estimation part  408 . When the temporal MAD storage part  416  performs estimation, comparison, and storing with respect to one picture, it transmits to the temporal noise calculation part  418  the temporal MADs of the respective sections as shown in the following table 4.  
                           TABLE 4                                       Section 1 (0 to 31)               Section 2 (32 to 63)   10           Section 3 (64 to 95)   26           Section 4 (96 to 127   22           Section 5 (128 to 159)   12           Section 6 (160 to 191)   24           Section 7 (192 to 223)   12           Section 8 (224 to 255)                      
 
         [0055]     The temporal noise calculation part  418  receives the table 4 from the temporal MAD storage part  416  and the table 2 from the section counter  410 . The temporal noise calculation part  418  calculates an average with respect to the temporal MADs based on table 4. The section having a counted value of 0 is not considered in calculating the average with respect to the temporal MADs. That is, the sections 1 and 8 are not considered in calculating the average with respect to the temporal MADs. The temporal noise calculation part  418  calculates the average simply based on the table 4. However, the temporal noise calculation part  418  may calculate the average by taking the counted values of the sections transmitted from the able 2 into consideration. Also, the temporal noise calculation part  418  may calculate the average as a temporal noise with respect to the temporal MADs excluding the least temporal MAD and the greatest temporal MAD.  
         [0056]     The temporal noise calculation part  418  transmits the calculated temporal noise to the noise calculation part  420 .  
         [0057]     The noise calculation part  420  outputs a smaller one of the spatial noise transmitted from the spatial noise calculation part  406  and the temporal noise transmitted from the temporal noise calculation part  418 . Also, the noise calculation part  420  may output an average of the spatial noise transmitted from the spatial noise calculation part  406  and the temporal noise transmitted from the temporal noise calculation part  418 . A value output from the noise calculation part  420  means a noise in the current image signal.  
         [0058]      FIG. 7  illustrates another example of a noise measurement apparatus  302   b  of the noise measurement apparatus  302  of  FIG. 3 , according to another embodiment of the present general inventive concept. Unlike the case of  FIG. 4 , a block average with respect to a current image signal and a block average with respect to a delayed image signal are transmitted to a temporal MAD estimation part  412 . Operations performed by a delayed block average estimation part  700  are identical to that performed by the block average estimation part  408 . The temporal MAD estimation part  412  receives a block average of each block, thereby reducing calculation amount. That is, since the temporal MAD estimation part  412  receives the block average of each block for the comparison, an amount of calculation can be reduced as compared to the temporal MAD estimation part  412  of  FIG. 4  which receives the pixels for the comparison.  
         [0059]     The present general inventive concept measures the spatial noise and the temporal noise at the same time, thereby reducing an error in noise measurement caused by a conventional apparatus which measures only the spatial noise with respect to the image having no plane area.  
         [0060]     Although a few embodiments of the present general inventive concept have been shown and described, it will be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the general inventive concept, the scope of which is defined in the appended claims and their equivalents.
A noise measurement apparatus and a method thereof capable of reducing an error in measuring a noise of incoming image signals. A picture of an incoming image signal is broken into at least two blocks and an average brightness value with respect to each block is calculated in a sequence. At least two first data, each being a sum of differences between the calculated average brightness value and brightness values of respective constituent pixels of the block, where the average brightness value is calculated, and a spatial noise is calculated based on the at least two first data. At least two second data that indicate a difference a brightness value of each block of the picture and a brightness value of each block of a delayed picture is calculated, and a temporal noise is calculated based on the at least two second data. A noise on the image signal is calculated based on the spatial noise and the temporal noise.