Abstract:
Disclosed is an apparatus and method for analyzing a histogram of an image, the apparatus comprising an image input module for buffering an input image, a block calculator for performing a blocking operation for dividing the image into pixel blocks of a predetermined size, and extracting and outputting a representative pixel of each block obtained in a corresponding blocking, a probability density function (PDF) operator for calculating a first histogram using all pixels of the image, and calculating a second histogram using the representative pixels input from the block calculator, and an image characteristic comparator for receiving the first and second histograms determining whether the received histograms satisfy an identity criterion and calculating a third histogram of a following image when the identity criterion is satisfied.

Description:
CLAIM OF PRIORITY 
     This application claims the benefit of the earlier filing date, under 35 U.S.C. 119(a), to that patent application entitled “Apparatus And Method For Histogram Analysis Of Image And Luminance Compensation Apparatus Using The Same,” filed in the Korean Intellectual Property Office on Feb. 8, 2006 and assigned Serial No. 2006-12144, the entire contents of which are incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to still image and a moving image processing, and more particularly to an apparatus and a method for analyzing a histogram, which represents the luminance characteristic and the contrast characteristic according to RGB colors of an image, and a luminance compensation apparatus using the same. 
     2. Description of the Related Art 
     In processing a still image and/or a moving image, it is necessary understand the characteristics of each corresponding image in order to apply a specific algorithm. In order to understand the characteristics of an image, a histogram analysis, which represents the luminance and contrast according to RGB colors of an image, is generally used. 
     According to the conventional computation method using luminance in order to analyze a histogram, luminance values of each pixel constituting a corresponding image in an input image are calculated, and the number of pixels having a corresponding luminance value is determined. The histogram is computing the number of pixels having a particular luminance value against the luminance values. Accordingly, the number of pixels as a function of specific luminance value is expressed in a graph by the histogram. In this case, when data of an input image correspond to data of an RGB domain, a histogram is computed after the data is mapped to a color space from which luminance components of the data can be extracted, or a histogram is computed with respect to each of RGB. Generally, the histogram computed as described above is used by converting histogram distribution into a monotonically increasing function, by accumulating the histogram distribution through a cumulative distribution function (CDF). 
       FIG. 1  is a block diagram schematically illustrating the construction of a conventional luminance compensation apparatus using a histogram analysis. The conventional luminance compensation apparatus includes a histogram analysis unit  10 , a luminance compensation function generation unit  11 , and a mapping unit  13 . The histogram analysis unit  10  includes a probability density function (PDF) operator, calculates luminance values of each pixel constituting a corresponding image in an input image, and calculates the number of pixels corresponding to each luminance value, thereby analyzing a histogram. The luminance compensation function generation unit  11  includes a cumulative distribution function (CDF), computes a cumulative distribution of a histogram, and generates a mapping function for luminance compensation based on the computed cumulative distribution. The mapping unit  13  compensates luminance values of an input image according to the mapping function generated by the luminance compensation function generation unit  11 . 
       FIGS. 2A to 2C  are graphs illustrating an exemplary histogram, cumulative distribution function, and mapping function of an image, respectively. As shown in  FIG. 2A , a histogram is obtained by classifying luminance values of an input image into 0 to 255 and making a graph showing the number of pixels as a function of each luminance value.  FIG. 2B  shows a cumulative distribution function generated by the luminance compensation function generation unit  11 . In this case, for example, when an input image has a resolution of 720×480, the cumulative distribution function has 345,600 final cumulative values.  FIG. 2C  shows a mapping function, which may be a function obtained by converting the cumulative distribution function into a 256-level gray scale. Consequently, such a mapping function has output luminance values corresponding to luminance values of input pixels. 
       FIG. 3  is a view illustrating an image frame for explaining a general histogram analysis method for an image. As indicated by the arrows in  FIG. 3 , when analyzing a histogram for an input image, the histogram analysis unit  10  uses a so-called pixel-by-pixel analysis method in which all pixels constituting the image are sequentially examined. 
     However, such an analysis method requires a large amount of calculation for analysis of a large still image or a high-resolution moving image. Particularly, devices such as mobile terminals have recently shown a tendency to reproduce a digital multimedia broadcasting (DMB) signal and a moving-image file, and also to be equipped with a liquid crystal display (LCD) capable of displaying a high-resolution image. Therefore, when the histogram analysis method as described above is used to analyze a high-resolution moving image or the like in a mobile terminal having a relatively poorer calculation capability, there is a problem in that a great amount of hardware resources are required. 
     SUMMARY OF THE INVENTION 
     Accordingly, the present invention has been made to solve the above-mentioned problems occurring in the prior art and provides additional advantages, by providing an apparatus and a method for efficiently performing a histogram analysis for an image with only a small amount of calculation, and a luminance compensation apparatus using the same. 
     In accordance with one aspect of the present invention, there is provided an apparatus for analyzing a histogram of an image, the apparatus comprising an image input module for buffering an input image, a block calculator for performing a blocking operation for dividing the image, which has been input to the image input module, into pixel blocks of a predetermined size, and extracting and outputting a representative pixel of each block obtained by corresponding blocking. a probability density function (PDF) operator for calculating a first histogram by using all pixels of the image, which has been input to the image input module, and calculating a second histogram by using the representative pixels input from the block calculator and an image characteristic comparator for receiving the first and second histograms output from the PDF operator, testing if the received histograms satisfy a predetermined identity criterion by comparing characteristics of the first histogram with characteristics of the second histogram, and calculating a third histogram of the input image in a scheme for the blocking when it has been determined as a result of the test that the predetermined identity criterion is satisfied. 
     In accordance with another aspect of the present invention, there is provided an apparatus for luminance compensation through a histogram analysis of an image, the apparatus comprising a histogram analysis unit for calculating a first histogram by using all pixels of an input image, performing a blocking operation for dividing the input image into predetermined pixel blocks, calculating a second histogram by using a representative pixel of each block obtained by the blocking, performing a test operation to determine if characteristics of the histograms satisfy a predetermined identity criterion, and calculating and outputting a histogram of an input image by using the representative pixels obtained by the blocking when it has been determined as a result of the test that the predetermined identity criterion is satisfied, a luminance compensation function generation unit for calculating cumulative distribution of the histogram, and generating a mapping function for luminance compensation based on the cumulative distribution and a mapping unit for compensating a luminance value of the input image according to the mapping function generated by the luminance compensation function generation unit. 
     In accordance with still another aspect of the present invention, there is provided a method for luminance compensation through a histogram analysis of an image, the method comprising the steps of calculating a first histogram by examining all pixels in an input predetermined image frames, and determining a type of the first histogram as a reference histogram type, calculating a second histogram by examining a representative pixel of each block obtained by blocking, which divides each of the input image frames into pixel blocks according to a plurality of predetermined sizes, and determining a type of the second histogram as a comparison histogram type, checking an identity between the reference histogram types and the comparison histogram types, and obtaining a block which has a largest size while satisfying an identity rate with respect to a predetermined reference value, and calculating a third histogram by performing a blocking operation for the input image frame based on the obtained largest block and actually outputting the third histogram. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above features and advantages of the present invention will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a block diagram schematically illustrating the construction of a conventional luminance compensation apparatus using a histogram analysis; 
         FIGS. 2A to 2C  are graphs illustrating an exemplary histogram, cumulative distribution function, and mapping function of an image, respectively, which has been obtained from the apparatus of  FIG. 1 ; 
         FIG. 3  is a view illustrating an image frame for explaining a general histogram analysis method for an image; 
         FIG. 4  is a block diagram illustrating the construction of a luminance compensation apparatus using a histogram analysis according to an embodiment of the present invention; 
         FIG. 5  is a view illustrating an image frame for explaining a histogram analysis method for an image according to an embodiment of the present invention; 
         FIG. 6  is a view illustrating various types of histograms for a histogram analysis according to an embodiment of the present invention; 
         FIGS. 7A to 7C  are views illustrating comparison test result lists with respect to a histogram analysis of the present invention and a conventional histogram analysis; and 
         FIG. 8  is a flowchart illustrating a histogram analysis procedure according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, one embodiment according to the present invention will be described with reference to the accompanying drawings. In the below description, many particular items such as a detailed component device are shown, but these are given only for providing a better understanding of the present invention. Therefore, it will be understood by those skilled in the art that various changes in form and detail may be made within the scope of the present invention. 
       FIG. 4  is a block diagram illustrating the construction of a luminance compensation apparatus using a histogram analysis according to an embodiment of the present invention. The luminance compensation apparatus as shown in  FIG. 4  includes a histogram analysis unit  20 , a luminance compensation function generation unit  11 , and a mapping unit  13 . 
     The histogram analysis unit  20  calculates a first histogram by using all pixels of an input image. Also, the histogram analysis unit  20  performs a blocking operation for grouping all pixels of the input image into 2×2 blocks, 4×4 blocks, or 8×8 blocks, and calculates a second histogram by using only representative pixels in each block obtained by the blocking operation. Then, the histogram analysis unit  20  performs a test operation to determine if the characteristics of the first and second histograms satisfy an identity criterion therebetween, and calculates a third histogram of an input image by examining only representative pixels obtained by the blocking when it is determined as a result of the test operation that the identity criterion is satisfied. 
     The luminance compensation function generation unit  11  calculates cumulative distribution of a histogram, and generates a mapping function for luminance compensation based on the cumulative distribution. The mapping unit  13  compensates luminance values of an input image according to the mapping function generated by the luminance compensation function generation unit  11 . The constructions and operations of the luminance compensation function generation unit  11  and mapping unit  13  may be substantially the same as, or equivalent to, those of corresponding components in the conventional apparatus shown in  FIG. 1 . 
     In detail, the histogram analysis unit  20  includes an image input module  201 , a probability density function (PDF) operator  202 , an image characteristic comparator  203 , and a block calculator  204 . The image input module  201  receives and buffers an input image. The block calculator  204  performs the blocking operation for dividing an input image into a block(s) (e.g., 2×2 blocks, 4×4 blocks, or 8×8 blocks), and extracts and outputs only a representative pixel of each block obtained by the blocking operation. The PDF operator  202  calculates the first histogram by using all pixels of the input image and calculates the second histogram by using only pixels input from the block calculator  204 . The image characteristic comparator  203  receives the first and second histograms output from the PDF operator  202 , and tests if the histograms satisfy a predetermined identity criterion by comparing the characteristics of the second histogram with the characteristics of the first histogram. When the first and second histograms satisfy the predetermined identity criterion, the image characteristic comparator  203  calculates a third histogram of the input image in the scheme for the blocking, and outputs the calculated histogram. 
       FIG. 5  is a view illustrating an image frame for explaining a histogram analysis method for an image according to an embodiment of the present invention. In  FIG. 5 , square blocks having a size which increases along a large broken arrow are shown as examples of blocking. Only representative pixels (e.g., any single pixel in a block, an average of all the pixels in the block, a weighted average of selected pixels in a block) obtained by each blocking operation are extracted as a corresponding block is moved in a direction indicated by the small solid arrow, and then histograms are calculated by using only the representative pixels. 
     In this case, since the first histogram is calculated by using all pixels of the image and the second histogram is calculated by using only representative pixels of an image obtained by the blocking operation a difference in the number of examined pixels exists and the two histograms cannot be equal to each other. However, the characteristics of the two histograms, i.e., the types of the histograms can be similar or substantially equal to each other. 
     As shown in  FIG. 6 , the types of first and second histograms may be classified into a plurality of types, i.e., into types A, B, C, D, E, and F. In  FIG. 6 , type A represents a histogram type of an image which includes a significant number of pixels having a middle luminance value, type B represents a histogram type of an image which includes a significant number of high luminance pixels, type C represents a histogram type of an image which includes a significant number of low luminance pixels, type D represents a histogram type of an image which includes a significant number of low luminance pixels and a significant number of high luminance pixels, type E represents a histogram type of an image in which pixels have uniform luminance, and type F represents a histogram type of an image in which pixels having a specific luminance value exist only in a part of the image. 
     The image characteristic comparator  203  ( FIG. 4 ) may determine that the above-mentioned identity criterion is satisfied only when the characteristic of the first histogram, calculated by using all pixels of an image of one frame, is the same as or substantially the same as that of the second histogram, calculated by using representative pixels of the image obtained by the blocking operation. However, particularly in the case in which an input image is a moving image, the image characteristic comparator  203  may determine that the above-mentioned identity criterion is satisfied, when several hundred to several thousand frames of images have been tested and 95% or more of the tested images have a characteristic satisfying the above-mentioned identity criterion. 
       FIGS. 7A to 7C  are views illustrating comparison test result lists with respect to a histogram analysis of the present invention and a conventional histogram analysis. Among the lists shown in  FIGS. 7A to 7C , a first clause of a list in  FIG. 7A  is as shown in Table 1. 
     
       
         
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
             
             
               
                 1 
                 1 Group: 4 
                 1 Group: 4 
               
               
                   
               
             
          
         
       
     
     In Table 1, “1” in a first column represents a frame number, and a first “1 Group: 4” in a second column represents that the type of a histogram calculated by using all pixels of a frame having a corresponding number (i.e., No. 1) corresponds to type 4. Also, a “1 Group: 4” in the second column represents that the type of histogram calculated by using representative pixels obtained by blocking of the frame having a corresponding number (i.e., No. 1) corresponds to type 4. As shown in Table 1, it can be understood as a result of comparison that the types of the two histograms are equal to each other. 
     Referring to  FIGS. 7A to 7C , it can be understood that the types of histograms only for frame Nos. 25 and 138 are not equal to each other. As described above, the image characteristic comparator  203  may test several hundred to several thousand frames of images in order to determine whether or not there is an identity between two types of histograms. 
     By the constructions and operations of the image input module  201 , PDF operator  202 , image characteristic comparator  203 , and block calculator  204 , as described above, the histogram analysis unit  20  can calculate a histogram of an input image by examining only representative pixels obtained by blocking of the input image, thereby improving a calculation performance for a histogram analysis. 
     Meanwhile, a blocking operation of the block calculator  204  for dividing an image into at least one known block size (2×2 blocks, 4×4 blocks, or 8×8 blocks) may be performed based on one preset block size, or may be established to set an optimum block size after testing blocks of various sizes, step by step. That is, it is possible that the block calculator  204  performs a blocking operation according to each block size, and the image characteristic comparator  203  checks the characteristics of each histogram calculated according to each block size and controls the block calculator  204  to perform a blocking operation by using the largest block which satisfies the identity criterion. 
       FIG. 8  is a flowchart illustrating a histogram analysis procedure according to an embodiment of the present invention. According to the histogram analysis procedure according to an embodiment of the present invention, when a moving image is input, a first histogram is calculated by examining all pixels, the characteristic of the first histogram (i.e., the type of the first histogram) is identified, and the type of the first histogram is determined as a reference histogram type (step  810 ). In step  820 , a second histogram is calculated by examining representative pixels obtained by a blocking operation, the type of the second histogram is identified, and the type of the second histogram is determined as a comparison histogram type. Also, in step  820 , it is possible to examine representative pixels according to each size of predetermined multiple blocks and determine comparison histogram types according to each size of the blocks. 
     The steps  810  and  820  may be repeated for each of a plurality of frames, for example, 1,500 frames. Thereafter, the reference histogram type is compared with each of comparison histogram types in order to determine whether or not there is an identity therebetween, thereby determining an optimum block size (step  830 ). That is, it is possible to obtain a block which has the largest size while satisfying an identity rate with respect to a reference value, and to determine the size of the obtained block as an optimum block size. In step  840 , a histogram of a following image is calculated by examining representative pixels of the following image based on the determined block size. 
     The histogram analysis operation according to an embodiment of the present invention may be performed as shown in  FIG. 8 , and such an operation may be performed according to each broadcasting program in the case of DMB signals. 
     As described above, the histogram analysis method for an image according to the present invention can increase the efficiency of a histogram analysis, which is required for grasping the characteristics of a still image or a moving image. Also, the histogram analysis method according to the present invention can be more efficiently utilized when the method is applied to a mobile terminal or the like, which has a relatively poorer calculation capability. 
     The processing described herein may be performed in a computing system or processor executing software instruction or code. The code may further be stored in a memory, e.g., RAM, ROM, in communication with the processor or computer system. The code may be loaded into the memory via a computer-readable medium, such as a CD. Or the code may be downloaded into the memory electronically through a network. 
     While the present invention has been shown and described with reference to certain preferred embodiments of the histogram analysis apparatus and method for an image and the luminance compensation apparatus using the same, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. For example, according to another embodiment of the present invention, it is possible that the histogram analysis unit performs a blocking operation for dividing an input image into 2×2 blocks, 4×4 blocks, or 8×8 blocks, and calculates histograms of following images by using only representative pixels of each block obtained by corresponding blocking, thereby further simplifying the construction of the histogram analysis unit. 
     Accordingly, the scope of the invention is not to be limited by the above embodiments but by the claims and the equivalents thereof.