JP6514481B2 - Image processing apparatus and program - Google Patents

Description

  The present invention relates to an image processing apparatus and program.

  Conventionally, in image processing, processing for quantizing an image may be performed. This quantization is a process of changing the number of bits of an image, such as converting a 10-bit image into an 8-bit image. The Lloyd-Max quantization method used at this time minimizes the difference between the image before quantization and the image after quantization at the time of quantization, thereby reducing image degradation due to quantization. It is one of the methods. In the Lloyd-Max quantization method, which is a type of scalar quantization, many gradations are assigned in the vicinity of gradation values with high luminance histogram frequency (for example, Non-Patent Document 1, Non-Patent Document 2, etc.) .

"Least squares quantification in PCM" S. P. Lloyd, IEEE Trans. Information Theory, vol. IT-28, pp. 129-136, Mar. 1982. "Quantizing for minimum distortion" J. Max, IEEE Trans. Information Theory, vol. IT-7, pp. 7-12, Mar. 1960.

  However, in the conventional Lloyd Max quantization method, there is a problem that the calculation cost becomes large because feedback processing is performed.

  One aspect of the present invention aims to reduce the computational cost during quantization.

The image processing apparatus according to one aspect, which generates a quantization table for quantizing image data, wherein the histogram indicates a relationship between a tone value of a pixel included in the image data and a frequency of a pixel serving as the tone value. Histogram generation means for generating data, and a threshold value table input for acquiring or generating a threshold value table in which the number of pixels allocated for each quantization value is set as a threshold value when assigning quantization values to pixels included in the image data Means, and in order from the lower one or the higher one of the gradation values indicated by the histogram data obtained from the histogram generation means, a number of quantization values corresponding to the count number of pixels for each of the gradation values are allocated assigned quantized values for all gradation values by switching the quantization value to the following quantization value if the number of quantized values reaches the threshold By assigning, anda quantization table generating means for generating a quantization table for quantizing the image data, the quantization table generating means, the image data is determined based on a result of the spectral analysis The maximum value of the number of pixels to which the quantization value of the pixel included in the gradation area is assigned is smaller than the maximum value for other quantization values, and the quantization table for quantizing the image data is generated again. I assume.

  The feed forward process can reduce the calculation cost in the quantization because the quantization can be performed without performing the repetitive operation.

  The image processing apparatus may further comprise analysis means for spectrally analyzing the image data, and the threshold value table input means may modify the threshold value table in which the threshold value is set according to the result of spectrum analysis by the analysis means.

  By the feedforward processing, quantization can be performed without performing repetitive operations, and calculation cost can be reduced at the time of quantization. Furthermore, the gradation region can be determined by being generated based on the result of the spectrum analysis.

  The analysis means determines a gradation area based on the value of the ratio of power of frequency from the result of the spectrum analysis, and the threshold table input means allocates the quantization value to the area determined to be the gradation area. It is characterized in that the threshold value table in which the ratio increases is acquired or generated.

  By the feedforward processing, quantization can be performed without performing repetitive operations, and calculation cost can be reduced at the time of quantization. Furthermore, the tone value can be finely quantized by being generated based on the result of the spectrum analysis.

  The threshold value table input unit may acquire or generate a threshold value table in which the threshold value is set so that the allocation ratio of the quantization value becomes equal.

  By the feedforward processing, quantization can be performed without performing repetitive operations, and calculation cost can be reduced at the time of quantization. Furthermore, the quantization values are assigned by a threshold table where the assignment rates are even. Therefore, the histogram can be smoothed by the threshold table in which the allocation ratio is even.

It is a block diagram explaining an example of the composition of the image processing system concerning one embodiment of the present invention. It is a functional block diagram explaining an example of functional composition of an image processing device concerning one embodiment of the present invention. It is a flow chart explaining an example of the whole processing by the image processing device concerning one embodiment of the present invention. It is a figure explaining an example of the histogram concerning one embodiment of the present invention. It is a figure explaining an example of the block field concerning one embodiment of the present invention. It is a figure explaining an example of gradation field analysis processing in spectrum analysis concerning one embodiment of the present invention. It is a figure explaining an example of the threshold value table for the 1st mode concerning one embodiment of the present invention. It is a figure explaining an example of the threshold value table for the 2nd mode concerning one embodiment of the present invention. It is a figure explaining an example of processing which generates a quantization table concerning one embodiment of the present invention. It is a figure explaining an example of the quantization table which concerns on one Embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described.

<Overall configuration>
FIG. 1 is a block diagram for explaining an example of the arrangement of an image processing system according to an embodiment of the present invention. An image processing system 1 shown in FIG. 1 includes an image processing apparatus 10 and a transmission apparatus 11.

  The image processing apparatus 10 uses the image data input to the image processing system 1 to generate a quantization table for quantizing an image by a histogram generation process, a spectrum analysis process, and the like. Quantizing an image means, for example, quantizing image data having a pixel gradation value of 10 bits (1024 gradations) into 8-bit image data (256 gradations). The number is not limited to this. In addition, since quantization is performed, the number of bits after quantization is smaller than the number of bits before quantization. Also, the image processing apparatus 10 may perform quantization processing of image data input using the generated quantization table.

  The image processing apparatus 10 is realized by, for example, an electronic circuit board or the like. For example, an FPGA (Field-Programmable Gate Array) or the like is mounted on the electronic circuit board, and various processes performed by the image processing apparatus 10 are executed by the mounted FPGA or the like. Note that the present embodiment is not limited to the case of using a PLD (Programmable Logic Device) such as an FPGA. The present embodiment may be configured such that all or part of the processing performed by the image processing apparatus 10 is realized by an application specific integrated circuit (ASIC), for example.

  Further, the image processing apparatus 10 is not limited to the electronic circuit board. The image processing apparatus 10 may be, for example, a PC (Personal Computer). When the image processing apparatus 10 is a PC, the image processing apparatus 10 may have a CPU (Central Processing Unit), and the CPU may execute various types of processing by executing a program.

  The transmission device 11 transmits various data such as processed image data (for example, 8-bit image data) output from the image processing device 10 and quantization table data to a reception device having a predetermined image restoration function.

  On the receiving side (not shown), the receiving device receives the image data transmitted by the transmitting device 11, the quantization table data, etc., and uses the received image data and the quantization table data to generate image data. Then, processing such as tone restoration is performed. The tone restoration process is, for example, a process of inversely quantizing image data having a tone value of 8 bits (256 tones) into 10-bit image data (1024 tones), but is not limited thereto. Absent. Further, in the present embodiment, processing other than tone restoration may be performed on the reception side.

<Example of Functional Configuration of Image Processing Device 10>
Next, a functional configuration example of the image processing apparatus 10 in the image processing system 1 described above will be described with reference to the drawings. FIG. 2 is a functional block diagram for explaining an example of the functional configuration of the image processing apparatus according to the embodiment of the present invention.

  The image processing apparatus 10 shown in FIG. 2 includes an input unit 10F1, a histogram generation unit 10F2, a spectrum analysis unit 10F3 as an example of an analysis unit, a threshold table input unit 10F4, a quantization table generation unit 10F5, and quantization. And means 10F6.

  The input unit 10F1 inputs image data, operation mode data, threshold table data, and the like. The operation mode is to set the operation contents of assigning the quantization value of 8 bits to the gradation value of 10 bits, for example, in the quantization table generation means 10F5, and the threshold value table is set according to the operation mode. Ru. The threshold table data may have a plurality of threshold tables corresponding to a plurality of operation modes set in advance.

  In addition, the input unit 10F1 divides the input image data into a predetermined block area serving as a reference for acquiring or generating a threshold value table, and a small block area serving as a reference for performing spectrum analysis in the predetermined block area. Divide into These divided data are output to spectrum analysis means 10F3 and threshold value table input means 10F4. The input unit 10F1 is realized by, for example, an interface for input / output and an electronic circuit such as an FPGA.

  The histogram generation unit 10F2 generates a histogram generation process that generates histogram data indicating the relationship between the tone value of the pixel included in the image data input by the input unit 10F1 and the frequency (count number) of the pixel serving as the tone value. Do. The histogram generation unit 10F2 is realized by an electronic circuit such as an FPGA.

  Spectrum analysis means 10F3 performs spectrum analysis of the image data input by the input means 10F1. For example, the spectrum analysis unit 10F3 analyzes image data for each small block area, and outputs an analysis result for each small block area. The spectrum analysis is performed using, for example, discrete cosine transform (DCT), fast Fourier transform (FFT), or the like. The small block region is, for example, a region of 8 pixels in the vertical axis direction, 8 pixels in the horizontal axis direction, or 16 pixels in the vertical axis direction, and 16 pixels in the horizontal axis direction, but is not limited thereto.

  The analysis result of the spectrum analysis is, for example, data of a frequency spectrum for a predetermined small block area, which is used to determine whether or not the small block area is a gradation area.

  The spectrum analysis means 10F3 can determine whether or not the small block area is a gradation area based on the value of the ratio of the power of the frequency from the result of the spectrum analysis. The spectrum analysis means 10F3 is realized by an electronic circuit such as an FPGA.

  The threshold value table input unit 10F4 performs processing for acquiring or generating threshold value table data. For example, when assigning a quantization value to a pixel included in image data, the threshold table input unit 10F4 acquires or generates a threshold table in which the number of pixels assigned for each quantization value is set as a threshold.

  Specifically, the threshold value table input unit 10F4 acquires or generates a threshold value table for each block area in accordance with the set operation mode. For example, it is a threshold value table in which the allocation ratio of quantization values is equal in one operation mode, and in the other operation mode, the equal quantization table is corrected according to the result of spectrum analysis.

  The correction of the quantization table is, for example, to increase the ratio of allocating the quantization value to the range of the gradation value determined as the gradation region by the result of the spectrum analysis. The threshold value table input unit 10F4 is realized by an electronic circuit such as an FPGA.

  The quantization table generation unit 10F5 generates a floor according to the count number of pixels for each gradation value indicated by the histogram data from the histogram generation unit 10F2 and the threshold value set in the threshold value table from the threshold value table input unit 10F4. A quantization value is assigned to each key value to generate a quantization table for quantizing image data. For example, the quantization table generation unit 10F5 quantizes the number corresponding to the count number of pixels for each gradation value in order from the low or high level of the gradation value indicated by the histogram data obtained from the histogram generation unit 10F2. Assign a value In addition, when the allocated quantization value reaches the threshold, the quantization table generation unit 10F5 switches to the next quantization value and allocates the quantization value to all the gradation values, thereby quantizing the image data. Generate the quantization table to be The quantization table generation unit 10F5 is realized by an electronic circuit such as an FPGA.

  The quantizing unit 10F6 quantizes, for example, 10-bit image data using the quantization table generated by the quantization table generation unit 10F5, and an 8-bit processed image data and a quantization table are shown in FIG. It is output to the transmitter 11. In the present embodiment, the image processing apparatus 10 may not have the quantizing unit 10F6, and in this case, the input 10-bit image data and the quantization table are output, The quantization table may be used in the device to generate 8-bit image data. In the present embodiment, the transmission device 11 of FIG. 1 transmits the quantization table data output from the image processing device 10 and the post-processing image data to a reception device or the like on the reception side.

  In addition, each part is not limited to the case where it is realized by an electronic circuit. Each part may be realized in whole or in part by a program.

  Conventionally, since Lloydmax quantization attempts to minimize the difference between an input image and an image after quantization by repeated operations, the calculation cost often increases. Since one embodiment of the present invention performs feedforward processing of assigning quantization values based on histograms and data of a threshold table, quantization can be performed without performing repetitive operations. By not performing repetitive calculations, the image processing apparatus 10 can reduce the calculation cost. In addition, since there is no processing that is repeatedly performed until convergence, the calculation time can be made constant, and when it is realized by an electronic circuit, control of the electronic circuit can be simplified.

<Overall processing>
FIG. 3 is a flowchart for explaining an example of the entire processing by the image processing apparatus according to the embodiment of the present invention.

  In the example of FIG. 3, in step S0201, the input unit 10F1 of the image processing apparatus 10 inputs image data. Next, in step S0202, the histogram generation unit 10F2 of the image processing apparatus 10 performs a histogram generation process for generating histogram data. FIG. 4 is a diagram for explaining an example of a histogram according to an embodiment of the present invention.

  As shown in FIG. 4, the histogram is a graph having “frequency” whose frequency is on the vertical axis, and “tone value” which is the luminance value of pixels in the image on the horizontal axis. The histogram generation process is performed for each block area obtained by dividing the image data into a predetermined size. Information for division into block areas is input to the image processing apparatus 10 in advance.

  FIG. 5 is a diagram for explaining an example of a block area according to an embodiment of the present invention. FIG. 5A is a view showing an example in the case of dividing image data according to an embodiment of the present invention into 3 × 3 9 block regions.

  As illustrated in FIG. 5A, the image 2 input in the input process is divided into three in each of the vertical and horizontal directions of the image. The block area 21 is preferably generated so that the number of pixels in each block area is equally divided so that the data amount of each histogram is the same for the same subject. The block area 21 is generated so that, for example, the area of the image 2 is equal to nine.

  The embodiment is not limited to the case of dividing into 3 × 3 9 block areas, and may be divided into 4 × 4 16 block areas as shown in FIG. 5B, for example.

  Returning to FIG. 3, next, in step S0203, the spectrum analysis unit 10F3 of the image processing apparatus 10 performs an analysis process of analyzing the spectrum of the image data. In spectrum analysis, image data of a block area input in input processing is analyzed for each small block area, and analysis results are output for each small block area. Further, in the spectrum analysis, it may be determined whether or not it is a gradation area based on the analysis result.

  FIG. 6 is a diagram for explaining an example of gradation region analysis processing in spectrum analysis according to an embodiment of the present invention.

  The example of FIG. 6 is a diagram showing an example where the small block area is 16 pixels in the vertical axis direction and 16 pixels in the horizontal axis direction. The gradation area analysis process performs, for example, DCT on the block area. Based on the result of the DCT, as shown in FIG. 6, the gradation area analysis process is a process of calculating the ratio of the power of the frequency at which both the horizontal frequency and the vertical frequency are 1⁄8 or less of the maximum frequency. The gradation area analysis process outputs the ratio as, for example, a value in the range of 0.00 to 1.00. When the value is large, the target block area is an area where the ratio of power in the low frequency band is high. The determination as to whether or not the small block area is a gradation area is implemented by, for example, a method of determining that the image processing apparatus 10 is a gradation area when the value of the power ratio is equal to or greater than a predetermined value.

  Returning to FIG. 3, next, in step S0204, the threshold value table input unit 10F4 of the image processing apparatus 10 performs processing for acquiring or generating a threshold value table. Here, in order to perform the process of step S0204, information for dividing into block areas is input to the image processing apparatus 10 in advance, as in step S0202. Further, in order to perform the process of step S0204, information related to step S0203 and the small block area is input to the image processing apparatus 10 in advance. Further, in order to perform the process of step S0204, the analysis result output in step S0203 is input to the image processing apparatus 10 in advance. Further, in order to perform the process of step S0204, the operation mode is input to the image processing apparatus 10 in advance by an external switch (not shown) or the like.

  For example, in step S0204, when the threshold value table is not obtained in advance from the outside, the threshold value table input unit 10F4 performs a process of generating the threshold value table. In addition, when the threshold value table is acquired in advance from the outside, the threshold value table input unit 10F4 performs a process of acquiring the threshold value table.

  In step S0204, in the process of generating the threshold value table, the image processing apparatus 10 can switch the process to be performed according to the operation mode to be input. The operation mode is, for example, a histogram smoothing mode (hereinafter, referred to as "first mode") to uniformly assign quantization values, and a pseudo contour suppression mode (hereinafter referred to as "second mode") to assign weights to gradation regions. Etc.) but is not limited thereto.

  In step S0204, the image processing apparatus 10 performs processing for each of the block areas 21 shown in FIG. 5 described above in either the first mode or the second mode.

  Hereinafter, the threshold of the first mode and the second mode described above will be described by taking, as an example, the case where the image processing apparatus 10 quantizes data of 1024 gradations of 10 bits into data of 256 gradations of 8 bits. Describe the table.

<Example of Threshold Table for First Mode>
FIG. 7 is a view for explaining an example of the threshold value table for the first mode according to the embodiment of the present invention. The items of the threshold value table shown in FIG. 7 are, for example, “quantized value (8 bits)”, “threshold value”, etc., but are not limited thereto. Here, the threshold is a value to be compared with the count number (frequency) for each gradation value. If the count number has reached the threshold, the image processing apparatus 10 starts processing for the next quantization value. The threshold is a value serving as a reference for switching the quantization value to be assigned.

  In the process of generating the threshold value table in step S0204, in the case of the first mode, as shown in FIG. 7, in the case of the first mode, the image processing apparatus 10 determines that the number of pixels allocated for each quantization value (hereinafter referred to as count number) is uniform. The threshold value is set to be Specifically, as shown in FIG. 7, the total number of pixels 256 × 256 = 65536 of the block area of the image data is divided by the total number 256 for eight bits, and the result (65526/256 = 256) is used as a threshold. Set As shown in FIG. 7, when the same threshold value is set for each quantization value, the image processing apparatus 10 generates the same number of pixels for each quantization value, and the image processing apparatus 10 quantizes the values. Assign evenly to each value. When the count number reaches the “threshold” shown in FIG. 7, the image processing apparatus 10 sets the quantization value to which the pixel is allocated as the next quantization value. The next quantization value is, for example, a quantization value obtained by adding 1 to the quantization value being assigned. For example, in the case of FIG. 7, when 256 pixels are assigned to the quantization value “0”, the image processing apparatus 10 performs processing of assigning the quantization value “1” to the next pixel. In the case of the first mode, the threshold value table can be generated without performing the processing in the spectrum analysis means 10F3 described above.

<Example of Threshold Table for Second Mode>
FIG. 8 is a view for explaining an example of the threshold value table for the second mode according to the embodiment of the present invention. The items in the threshold value table shown in FIG. 8 are similar to those in FIG. 7 described above, but are not limited thereto.

  In step S0204, in the case of the second mode, the image processing apparatus 10 determines whether or not the block area is a gradation area of the small block area. In the case of the gradation area, the range of the quantization value which is the gradation area is determined.

  The determination as to whether or not the area is a gradation area is performed by the image processing apparatus 10 based on the analysis result of the spectrum analysis of step S0203. For example, in the frequency spectrum obtained as an analysis result, the image processing apparatus 10 determines a smooth region without high frequency as a gradation region. Also, the determination is realized by, for example, the method described in FIG. 6 described above. The image processing apparatus 10 determines that, for example, at the frequency of the small block area, the sum of the lower 10% powers of the frequency is a gradation area when it exceeds 98% of the total power.

  When the small block area is determined to be a gradation area, the image processing apparatus 10 assigns a threshold to the gradation area by weighting.

  In the example of FIG. 8, as in FIG. 7, an example in which processing is performed for each of the block areas 21 of FIG. 5 is shown. The example of FIG. 8 is a diagram for explaining a threshold value table used in the second mode and when it is determined that the small block area 21G included in the block area 21 is a gradation area.

  FIG. 8 differs in that the threshold for the gradation area is set to be smaller than the threshold in FIG. FIG. 8 shows an example where the quantization values "100" to "102" are for the gradation area.

  In the case of FIG. 8, the threshold value corresponding to the small block area 21G which is the gradation area is set in the threshold value table 3 as a value smaller by “α” than in the case of FIG. That is, as shown in the threshold table 3 of FIG. 8, the threshold for the gradation area is set to a value obtained by subtracting “α” from the threshold set to be equal in the first mode. When the threshold value is a small value, the count number quickly reaches the threshold value, and the quantization value assigned to the generated pixel is often switched finely. In the case of FIG. 8, for example, the number of pixels allocated to the quantization value “100” is a number smaller than 256. Compared to the case of FIG. 7, in the case of FIG. 8, even in the case of a similar image, the number of pixels assigned to the quantization value “100” decreases.

  Therefore, in the case of FIG. 8, the image processing apparatus 10 can increase the possibility of assigning to another quantization value even if the pixel is a pixel of a luminance value close to that of the case of FIG. That is, the ratio of assigning the quantization value to the area determined to be the gradation area increases. When another quantization value is assigned, each pixel is output as a different output value, so a device (not shown) that performs output has pixels whose luminance values are close to each other like a gradation region. It is possible to express that there is a change in the luminance value by finely changing the output value instead of uniformly outputting the same output value even in a place where the two are close.

  In the second mode, as shown in the threshold table 3 of FIG. 8, the gradation area can be expanded before and after the quantization values “100” to “102” by reducing the threshold, and further, outside the gradation area Each threshold value in the gradation area can be finely quantized by increasing the threshold value of {circumflex over (d)}, and false contours can be suppressed.

  Further, in the case of FIG. 8, since the pixels assigned to the quantization values “100” to “102” are set to decrease “α” respectively, the quantization values “100” to “102” for the gradation region are set. The pixels assigned to “1” are “3α” less in total as compared with the case of FIG. For this reason, it is possible to finely quantize each quantization value in the gradation region, and it is possible to suppress pseudo contours. In the second mode, pixels are allocated to the quantization values “0” to “99” and the quantization values “103” to “255” other than for the gradation area in an amount reduced by “3α”. The assignment to the quantization value other than that for the gradation area is performed, for example, such that assigned pixels are equally assigned to the respective quantization values.

  Returning to FIG. 3, next, in step S0205, the quantization table generation unit 10F5 of the image processing apparatus 10 performs a process of generating a quantization table. In the process of step S 0205, the analysis result of step S 0203 and the threshold value table generated in step S 0204 or the threshold value table to be input are used. The process of generating the quantization table is, for example, a process of assigning quantization values to each of the block areas 21 of FIG.

<One example of processing to generate quantization table>
FIG. 9 is a diagram for explaining an example of a process of generating a quantization table according to an embodiment of the present invention. The process of generating the quantization table will be described by way of example in which 8-bit quantization values 100 to 102 are assigned to 10-bit gradation values 400 to 407 shown in FIG. 9A.

  FIG. 9A shows an example of the analysis result of step S0202. FIG. 9A shows an example of a histogram of the block area 21 of FIG. In the process of generating the quantization table, the quantization value is assigned, for example, from a low gradation value. In addition, you may allocate from the higher one.

  FIG. 9B is a diagram for explaining an example of assignment of the quantization value “100”. FIG. 9B shows the case where the quantization value “100” is assigned in order from the lowest value, ie, the 10-bit gradation value 400. The amount to be allocated is determined by the threshold value table generated in step S0204 or the threshold value set to the quantization value “100” of the input threshold value table.

  In the example of FIG. 9B, it is shown that up to the hatched portion (in the middle of the gradation value 402) is allocated until the threshold value corresponding to the quantization value "100" is reached.

  In step S0205, as shown in FIG. 9B, the quantization table generation unit 10F5 applies the quantization value "100" to the next quantum when the allocation of the quantization value "100" reaches the corresponding threshold value. The allocation value is switched to the conversion value "101". FIG. 9C is a diagram for explaining an example of assignment of the quantization value "101". In FIG. 9C, after assigning the quantization value “100” in FIG. 9B, the quantization value “101” is quantized in order from the middle of the 10-bit gradation value 402 which is the lowest value. Assignment is performed until the threshold value of “101” is reached, indicating that the tone value 403 has been assigned halfway.

  FIG. 9D is a view for explaining an example of assignment of the quantization value “102”. Similar to FIGS. 9B and 9C, when the allocation of the quantization value “101” in FIG. 9C reaches the threshold, the process of FIG. 9D performs the next quantization. It is a process of switching to the value "102" and performing allocation sequentially from the middle of the 10-bit gradation value 403 which is the lowest value.

  In the example of FIG. 9D, the quantization value "100" and the quantization value "101" are assigned to the gradation value 402, but in this case, for example, the previously assigned quantum Prioritize the conversion value "100". Therefore, the quantization value "100" is set to the gradation value 402 of the quantization table. The setting method is not limited to the above example.

  In the first mode, the assignment process described above is a process performed based on the “threshold value” set in the threshold value table 3 shown in FIG. 7. In the case of the second mode, the process of assignment is a process performed based on the "threshold" set in the threshold table 3 shown in FIG. As illustrated in FIG. 9B, the image processing apparatus 10 counts the number of pixels allocated to the quantization value “100” when allocating the pixel of the gradation value “400” to the quantization value “100”. Perform processing to count as a number. That is, each time there is a pixel assigned to the quantization value “100”, the image processing apparatus 10 performs a process of counting up the count number. When the count number reaches the “threshold value” set in the threshold value table 3 shown in FIG. 7 or FIG. 8, the image processing apparatus 10 allocates a pixel to the quantization value “101” which is the next quantization value.

  When the image processing apparatus 10 assigns pixels to the quantization value "101", the image processing apparatus 10 counts the number of pixels to be assigned to the quantization value "101" as the count number, as in the case of the quantization value "100". Perform processing to count. The process of assigning a pixel to the quantization value "101" is set to the quantization value "101" based on the threshold value table 3 of FIG. 7 or 8 as in the case of the quantization value "100". Is a process performed until the count number reaches.

  As shown in FIG. 9, in the process of generating the quantization table, quantization values are assigned to the tone values of the histogram based on the threshold value table, and finally the quantization values are assigned to all the tone values. assign.

  In addition, since it is desirable that pixels located in the same division area and having the same luminance value be assigned to the same quantization value, they may be aligned and assigned to a larger number of quantization values. For example, in the case of FIG. 9D, a case where there are a plurality of pixels having a gradation value of "402" will be described as an example. When there is a pixel located in the same divided area among the pixels which are the gradation value of "402", all the pixels which are the gradation value of "402" located in the same divided area are aligned to the same quantized value It is desirable to be assigned. For example, it is desirable that all the pixels which are the gradation value of "402" located in the same division area be aligned and assigned to the quantization value "100". Since pixels located in the same block area and having the same luminance value often exhibit the same subject, that is, the same color, it is often desirable to output with the same quantization value. Therefore, the image quality can be improved by allocating the pixels located in the same block area and having the same luminance value to the same quantization value so as to be output with the same quantization value. However, the assignment process is not limited to the method described above. The assignment process may be a process of specifying the position of each pixel by another method and performing assignment for each position of each pixel.

  By assigning quantization values based on a histogram, many quantization values can be assigned in the vicinity of frequently occurring tone values.

  When the operation mode is the first mode described above, the threshold value table is set so that the number of pixels assigned to each quantization value is equal. Therefore, in the case of the first mode, the image processing apparatus 10 can smooth the histogram by the threshold value table.

  When the operation mode is the second mode described above, the threshold value table is set so that the quantization value assigned to the gradation area is large. Therefore, in the case of the second mode, the image processing apparatus 10 can finely quantize the gradation value by the threshold value table.

  Here, setting of a range in which the threshold value in the second mode is reduced will be described. First, the range of the gradation area is obtained by the value of 10-bit gradation value. Assuming that this range is, for example, "400" to "419", the range of 8-bit gradation values to which this range falls is obtained by the procedure of FIG. 9 once assuming that it is the first mode. This corresponds to, for example, “110” to “115”, and if the threshold of this range is reduced to half (256/2) because it is a gradation region, the gradation region will be the upper and lower portions by the reduced amount. The range of bit gradation values needs to be expanded. That is, three quantization values (the threshold value is 256/2) are extended to the gradation area on the upper side and the lower side, respectively, and eventually the gradation area of 8 bits becomes “107” to “118”. Further, the remaining “0” to “106” and “119” to “255” of quantization values are equally heavily burdened by the pixel (256/2 × 12) reduced from the uniform (the threshold value increases).

  The present embodiment is not limited to the entire processing shown in FIG. In the present embodiment, each process may be processed in parallel or in a distributed manner. For example, the process of step S0202 and the process of step S0203 may be performed in parallel.

<Example of quantization table>
FIG. 10 is a diagram for explaining an example of a quantization table according to an embodiment of the present invention.

  The quantization table 4 is an example of quantization table data generated when the allocation described in FIG. 9 is performed. That is, the quantization table is the vertical axis for each 10-bit gradation value on the horizontal axis in FIG. 9 after allocation to 8-bit quantization values and alignment to the same quantization value for the same luminance value are completed. It is a correspondence table of the allocated 8-bit gradation value.

  As illustrated in the quantization table 4, when the quantization value "100" is assigned to the gradation value "400" in FIG. 9B, the quantization table 4 is a quantization value for the gradation value "400". "100" is set to. Similarly, quantization values determined by the allocation described in FIG. 9 are respectively set for the gradation values “401” to “405”.

  Returning to FIG. 3, next, in step S0206, the quantizing unit 10F6 of the image processing apparatus 10 performs a process of quantizing the image data using the generated quantization table. Next, in step S0207, the quantizing unit 10F6 of the image processing apparatus 10 performs a process of outputting the processed image data and the quantization table data to the transmission apparatus 11 of FIG.

  The transmitter 11 of FIG. 1 transmits the quantization value set in the quantization table 4 to the receiving side as quantization table data. In each quantization value of the quantization table data, data corresponding to one row of the quantization table 4 corresponds to a gradation value of 10 bits. In the case of 10 bits, the gradation value is 1024 steps. In the case of the 10-bit gradation value and the RGB three-color color, the quantization table data is data indicating 1024 steps × 3 colors = 3072 quantization values.

  On the reception side, the reception device performs tone restoration processing on the pixels included in the image data to be received, based on the quantization table data. Specifically, in the case of FIG. 10, on the reception side, the reception apparatus performs, for example, processing to set an 8-bit pixel of gradation value "100" included in image data to a 10-bit pixel of "400". For example, when 8-bit tone values correspond to a plurality of 10 bits, the median value of the 10-bit tone values is selected.

<Execution program>
Here, the image processing apparatus 10 described above may be, for example, a volatile storage medium such as a central processing unit (CPU) or a random access memory (RAM), a nonvolatile storage medium such as a read only memory (ROM), a mouse, It can be configured by a computer provided with an input device such as a keyboard, a pointing device, a display unit for displaying images and data, and an interface for communicating with the outside.

  Therefore, each function of the image processing apparatus 10 can be realized by causing the CPU to execute a program in which these functions are described. Also, these programs can be stored and distributed in a recording medium such as a magnetic disk (floppy disk, hard disk, etc.), optical disk (CD-ROM, DVD, etc.), semiconductor memory, etc.

  That is, the image processing according to the present embodiment can be realized by generating an execution program for causing a computer to execute the processing in each configuration described above and installing the program in, for example, a general-purpose PC or server.

  As described above, according to the present embodiment, it is possible to reduce the calculation cost at the time of quantization. Further, according to the present embodiment, it is possible to assign a large number of quantization values in the vicinity of the tone value having a high appearance frequency, and generate a quantization table by feedforward processing in consideration of histogram smoothing.

  Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the specific embodiments, and various modifications may be made within the scope of the present invention as set forth in the claims. , Change is possible.

Reference Signs List 1 image processing system 10 image processing apparatus 10F1 input unit 10F2 histogram generation unit 10F3 spectrum analysis unit 10F4 threshold table input unit 10F5 quantization table generation unit 10F6 quantization unit 11 transmitter 2 image 21 block area 21G gradation area 3 threshold table 4 quantum Table

Claims (2)

An image processing apparatus for generating a quantization table for quantizing image data, comprising:
A histogram generation unit configured to generate histogram data indicating a relationship between a tone value of a pixel included in the image data and a frequency of a pixel serving as the tone value;
A threshold value table input unit configured to obtain or generate a threshold value table in which the number of pixels to be assigned to each of the quantization values is set as a threshold when assigning quantization values to pixels included in the image data;
The allocated quantum value is a number of quantization values corresponding to the count number of pixels for each of the gradation values in order from the lower one or the higher one of the gradation values indicated by the histogram data obtained from the histogram generation means by assigning a quantization value for all the gradation values by switching the quantization value to the following quantization values if the number of reduction value reaches the threshold value, quantization for quantizing the image data Quantization table generation means for generating a table ;
Have
The quantization table generation means
In the image data, the maximum value of the number of pixels to which the quantization value of the pixel included in the gradation area determined based on the result of the spectrum analysis of the image data is allocated is smaller than the maximum value for other quantization values. An image processing apparatus for generating again a quantization table for quantizing . A program that causes a computer to function as the image processing apparatus according to claim 1.