JPH09307897A - Image data compression processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To compress image data at a high compression rate without deteriorating image quality of an important part of an original image and to reduce the arithmetic time in the image data compression processing method. SOLUTION: Weblet transformation 2 is applied to original image data 1 representing an original image to obtain image data 3 for each of a plurality of frequency bands. Then histogram analysis is applied to the image data 3, importance is recognized (5) by each density band in the image based on the analysis result, and a table conducting quantization with higher bit number as importance is higher is selected among a plurality of quantization tables used to conduct quantization by different bit numbers. The quantization 7 is applied to the image data 3 based on the selected table. The coding 8 is applied to the image data 3 to which quantization 7 is applied.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image data compression processing method, and more particularly, to an image data compression processing method capable of obtaining a high data compression rate by using multi-resolution conversion.

[0002]

2. Description of the Related Art Since an image signal carrying a halftone image such as a TV signal has an enormous amount of information, a wide band transmission line is required for its transmission. Therefore, conventionally, attention has been paid to the fact that such an image signal has high redundancy, and various attempts have been made to compress the image data by suppressing this redundancy. Further, recently, for example, recording of a halftone image on an optical disk or a magnetic disk has been widely performed, and in this case, image data compression is widely applied for the purpose of efficiently recording an image signal on a recording medium. ing.

As one of such image data compression methods, conventionally, when image data is stored or transmitted, the image data is compressed by predictive coding to reduce the amount of data. Store, transmit, etc. on
At the time of image reproduction, there is a method of performing decoding processing on the compressed image data (compressed image data) to decompress it and reproducing a visible image based on the decompressed image data (decompressed image data). Has been adopted.

A method utilizing vector quantization is known as one of image data compression processing methods. This method divides the two-dimensional image data into blocks of K samples and predefines K vector elements to create a codebook composed of a plurality of different vectors. The vector corresponding to the set of image data and the minimum distortion is selected, and the information indicating the selected vector is encoded in association with each block.

Since the image data in the blocks as described above have a high correlation with each other, one of the vectors in which a relatively small number of image data in each block are prepared is prepared.
It is possible to give a fairly accurate representation by using one. Therefore, transmission or recording of image data can be performed by transmitting or storing a code indicating this vector instead of actual data, so that data compression is realized. For example, the amount of image data for 64 pixels in a halftone image of a 256-level (= 8 bit) density scale is 8 × 64 = 512 bits, and these 64 pixels are regarded as one block, and each image data in the block has 64 elements. If a codebook is prepared in which 256 types of such vectors are prepared, the data amount per block is the data amount for vector identification, that is, 8 bits, and the data amount is 8 / ( 8 x 6
4) = 1/64 can be compressed.

After the image data is compressed and recorded or transmitted as described above, the vector element of the vector indicated by the vector identification information is used as reconstruction data for each block, and the original image is reproduced by using this reconstruction data. To be done.

Further, as one of the methods for improving the compression rate in the case of performing the data compression by the above-mentioned predictive coding,
It is conceivable to reduce the bit resolution (density resolution) of the image data together with the predictive coding process, that is, perform the quantization process of coarsely quantizing the image data.

Therefore, the applicant of the present invention has proposed an image data compression processing method by interpolation coding, which is a combination of the above-described prediction coding method and quantization method (Japanese Patent Laid-Open No. 62-247676). . In this method, image data is divided into main data sampled at appropriate intervals and interpolation data other than the main data, and the interpolation data is interpolation prediction coding processing based on the main data, that is, interpolation data is the main data. The image data is compressed by performing interpolative prediction based on the above, and performing variable length coding (conversion into a signal whose code length changes depending on the value) such as Huffman coding with respect to the prediction error.

Further, when compressing image data, it is naturally desirable that the compression rate is high. However, it is technically difficult to expect a large improvement in the compression rate in the above-mentioned interpolation coding, and therefore, in order to achieve a higher compression rate, the image data number reduction processing for reducing the spatial resolution is combined with the above-mentioned interpolation coding. It is possible to match.

Therefore, the applicant of the present invention has proposed an image data compression processing method capable of achieving a higher compression rate while maintaining high image quality by combining the above-described interpolation coding and the image data number reduction processing (special feature). Kaihei 2-280462).

On the other hand, as a method for processing the above-mentioned image data, the image is converted into a multi-resolution image for each of a plurality of frequency bands, a predetermined process is performed on the image in each frequency band, and the image is again processed. A method called multi-resolution conversion for obtaining a final processed image by performing inverse multi-resolution conversion has been proposed. Known methods for this multi-resolution conversion include wavelet conversion, Laplacian pyramid, and Fourier conversion.

Here, the wavelet transform will be described.

The wavelet transform has been recently developed as a method of frequency analysis and has been applied to stereo pattern matching, data compression, etc. (OLIVIER RIOUL and MARTIN VETTERLI; Wavelets a
nd Signal Processing, IEEESP MAGAZINE, P.14-38, OCTOB
ER 1991, Stephane Mallat; Zero-Crossings of a Wavel
et Transform, IEEE TRANSACTIONS ON INFORMATION THEO
RY, VOL.37, NO.4, P.1019-1033, JULY 1991).

This wavelet transform uses a function h as shown in FIG. 12 as a basis function.

[0015]

[Equation 1]

Since the signal is converted into a frequency signal for each of a plurality of frequency bands in the equation, the problem of false vibration unlike the Fourier transform does not occur. That is, if the filtering process is performed by changing the period and reduction ratio of the function h and moving the original signal, it is possible to create a frequency signal adapted to a desired frequency from a fine frequency to a coarse frequency. For example, as shown in Figure 13, the signal So
rg is wavelet transformed and inverse wavelet transformed for each frequency band and signal Sor as shown in FIG.
Looking at a signal obtained by Fourier transforming g and performing inverse Fourier transform for each frequency band, the wavelet transform can obtain a frequency signal in a frequency band corresponding to the oscillation of the original signal Sorg, as compared with the Fourier transform. That is, in the Fourier transform, vibration occurs in the part B'of the frequency band 7 corresponding to the part B of the original signal Sorg, whereas in the wavelet transform, the part of the frequency band W7 corresponding to the part A of the original signal Sorg. As in the original signal, no vibration is generated in A '.

Further, using this wavelet transform,
A method for compressing the above-mentioned image data has been proposed (Marc Antonini et al., Image Coding Using Wavelet.
Transform, IEEE TRANSACTIONS ON IMAGE PROCESSING
, VOL.1, NO.2, p205-220, APRIL 1992).

According to this method, original image data representing an image is subjected to wavelet transformation to convert the original image data into image data of a plurality of frequency bands, and each image data has a high frequency which carries a lot of noise components. Image data in a band has a small number of bits, and image data in a low frequency band carrying information on a main subject is assigned a large number of bits to perform the above-mentioned vector quantization to compress the original image data. Is. According to this method, the compression rate of the original image data can be improved, and the original image can be completely restored by performing the inverse wavelet transform on the compressed image data.

On the other hand, the method of Laplacian pyramid is described, for example, in Japanese Patent Laid-Open Nos. 5-244508 and 6-301766. After the mask processing is performed, the image is sub-sampled and the number of pixels is thinned to halve it to obtain a blurred image having a size of ¼ of the original image. Interpolate the pixels of to return to the original size image,
This image is further masked by the mask described above to obtain a blurred image, and this blurred image is subtracted from the original image to obtain a detailed image representing a predetermined frequency band of the original image. By repeating this process for the blurred image obtained, N blurred images each having a size of 1/2 ^2N of the original image are created. Here, since an image masked by a mask similar to a Gaussian function is sampled, a Gaussian filter is actually used, but the same processing as when a Laplacian filter is applied is performed. A finished image is obtained. Since an image in the low frequency band having a size of 1/2 ^2N is sequentially obtained from the image of the original image size in this manner, the image obtained as a result of this processing is called a Laplacian pyramid.

Regarding this Laplacian pyramid, Burt PJ, "Fast Filter Transforms for Image
Processing ”, Computer Graphics and Image Process
ing Vol. 16, pp. 20-51, 1981; Crowley JL, Stern R.
M., “Fast Computation of the Difference of Low ・ Pa
ss Transform ”IEEETrans.on Pattern Analysis and Mac
hine Intelligence, Volume 6, Issue 2, March 1984, Mallat
SG, “A Theory for Multiresolution Signal Decompos
ition ； The Wavelet Representation ”IEEETrans.on P
attern Analysis and Machine Intelligence, Volume 11,
No. 7, July 1989; Ebrahimi T., Kunt M., “Image comp.
ression by Gabor Expansion ”, Optical Engineering,
Volume 30, Issue 7, pages 873-880, July 1991, and Pieter.
Vuylsteke, Emile Schoeters, “Multiscale Image Con
trast Amplification ”SPIEVol.2167 Image Processin
g (1994), pp551-560, for details.

By the way, in the method of compressing image data using the multi-resolution conversion as described above, if the compression rate is further improved, the image quality of the original image may be deteriorated and the image quality may be improved. There was a limit to the compression ratio. On the other hand, in the case of quantizing image data, if the quantization width when quantizing is made fine, the data compression rate will decrease, but it can be compressed in a state closer to the original image, so it was reconstructed. The image quality of the image is less deteriorated. On the other hand, if the quantization width is made coarse, there will be a large error when decompressing the compressed image data, and this error will appear in the image as noise when the image is decompressed. Although the deterioration is large, the data compression rate can be improved because the code used for encoding becomes shorter.

Therefore, the applicant of the present invention recognizes the importance of each part of the image in the image data decomposed into a plurality of frequency bands by the wavelet transform, labels the image according to the importance, and determines the importance. An image data compression method has been proposed in which a high quantization area is quantized with a fine quantization width, and a low importance area is quantized with a coarser quantization width as compared with a high importance area. Kaihei 6-350989 publication). According to this method, the image data can be compressed while maintaining the image quality for the important part of each part in the image, and the image data can be compressed at a higher compression rate for the unimportant part. be able to. Therefore, the compression rate of the image data can be improved without degrading the image quality of an important part of the image.

That is, in the quantization of the high frequency component, the degree of importance is obtained from the original image or the low frequency image, and the quantization width is changed according to the obtained degree of importance. In addition, high-frequency component encoding is performed using one Huffman table, and high-frequency component decoding is performed using one Huffman table, similar to encoding. In the inverse quantization, since the quantization table is not switched at the time of the quantization, the inverse wavelet transform can be performed as it is. Therefore, it is possible to perform high-speed decoding without performing case classification at the time of decoding.

[0024]

However, in the method described in Japanese Patent Laid-Open No. 6-350989, although the compression rate can be improved, it is desired to further improve the compression rate.

In view of the above circumstances, it is an object of the present invention to provide an image data compression processing method capable of further improving the compression rate.

[0026]

A first image data compression processing method according to the present invention is an image data compression processing method for performing compression processing on original image data representing an image including a predetermined subject. By performing the multi-resolution conversion, the original image data is decomposed into image data for each of a plurality of frequency bands, and a histogram analysis is performed on each of the image data to recognize the importance of each density band in the image. , A quantization table corresponding to the density band in the image is selected from among a plurality of predetermined quantization tables according to the importance, and each image data is quantized by the selected quantization table. The image data is quantized and the quantized image data is encoded.

A second image data compression processing method according to the present invention is an image data compression processing method for performing compression processing on original image data representing an image including a predetermined subject,
By applying multi-resolution conversion to the original image data,
The original image data is decomposed into image data for each of a plurality of frequency bands, and the importance of each differential value in the image is recognized by performing a differential process on each of the image data, and a plurality of predetermined quantum From the quantization table, select a quantization table corresponding to the differential value in the image according to the importance, quantize each image data by the selected quantization table, the quantized the It is characterized in that each image data is encoded.

Further, the image data reconstruction method according to the present invention is a method for reconstructing the compressed image data obtained by the first or second image data compression processing method according to the present invention, wherein the encoding is performed. The original image data is reconstructed by decoding each of the decoded image data, performing inverse quantization on the decoded image data, and performing inverse multiresolution conversion.

[0029]

The image data compression processing method according to the present invention recognizes the importance of each part of the image based on the result of the histogram analysis or the differential processing for the image data decomposed into a plurality of frequency bands by the multi-resolution conversion, A table is selected according to the degree of importance from a plurality of quantization tables having different quantization widths, and the selected table is used for quantization. That is, a quantization table that performs a fine quantization is selected for a high importance part, and a low quantization part performs a quantization with a coarser quantization width as compared to a high importance part. The table is selected. Therefore, it is possible to compress the image data while maintaining the image quality in the important part of each part in the image, and to compress the image data in the unimportant part with a higher compression rate. At the same time, in the Huffman coding after the quantization, since the coding can be performed using one Huffman table, it is possible to prevent the calculation from becoming complicated. Therefore, the compression rate of the image data can be improved without degrading the image quality of an important part of the image as a whole.

The second image data compression processing method according to the present invention is such that the degree of importance in the image is recognized by the differential processing. For example, for a portion having a large differential value such as a character in an image, unless the quantization is finely performed, the image deterioration becomes very noticeable. Therefore,
By recognizing the degree of importance by differentiating processing, it is possible to select a quantization table that performs fine quantization with respect to a portion with a large differential value such as a character, thereby reducing image deterioration. Then, the image data compression process can be performed. On the other hand, for a relatively large differential value such as a grayscale image, the image deterioration is not so noticeable even if the quantization is roughened.Therefore, select a quantization table for performing quantization with a rough quantization width, As a result, the image data compression process can be performed with a relatively high compression rate.

In the image data reconstruction method according to the present invention, the image data compressed by the first or second image data compression processing method according to the present invention is decoded, and an inverse quantum is applied to the decoded image data. Since the conversion is performed and the inverse wavelet transform is performed, the original image can be reproduced while maintaining the image quality of an important part of each part of the image.

[0032]

Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a diagram showing the basic concept of an image data compression processing method according to the present invention. As shown in FIG. 1, in the image data compression processing method according to the present invention, an original image data 1 representing an original image is subjected to wavelet transform 2, which is one method of multi-resolution conversion, to obtain an image for each of a plurality of frequency bands. Obtain data 3. Next, the histogram analysis 4 is performed on each image data 3, the importance 5 is recognized for each density band in the image based on the result of the histogram analysis, and the quantization width is determined according to the importance. The selection 6 of the quantization table corresponding to the density band in the image is performed from the plurality of different quantization tables. Then, the image data 3 is quantized 7 by the selected quantization table, and the quantized image data 3 is encoded 8.

The details of the embodiment according to the present invention will be described below.

This embodiment is a storage system for a radiation image information recording / reproducing system using a storage phosphor sheet recorded in, for example, JP-A-55-12492 and JP-A-56-11395. It is intended to read a radiation image of a human body recorded on a phosphor sheet as digital image data by laser beam scanning. In addition, as shown in FIG. 2, the radiation image is read by the stimulable phosphor sheet.
The sheet 10 is two-dimensionally scanned by moving the sheet 10 in the sub-scanning direction (vertical direction) while scanning the laser beam in the main scanning direction (horizontal direction) with respect to 10.

Next, wavelet transform is performed on the original image data.

FIG. 3 is a diagram showing the details of the wavelet transform for the original image data Sorg. In addition, in the present embodiment, the orthogonal wavelet transform in which each coefficient of the wavelet transform is orthogonal is performed.
It is described in the article by Marc Antonini et al.

As shown in FIG. 3, filtering processing is performed in the main scanning direction of the original image data Sorg using the functions g and h obtained from the basic wavelet function. That is, the filtering process for each column of pixels arranged in the main scanning direction by such functions g and h is performed while shifting pixel by pixel in the sub scanning direction to obtain the wavelet transform coefficient signal Wg0 of the original image data Sorg in the main scanning direction. This is for obtaining Wh0.

Here, the functions g and h are uniquely obtained from the basic wavelet function. For example, the function h
Is shown in Table 1 below. It should be noted that in Table 1, the function h'represents a function used when performing inverse wavelet transform on image data that has been subjected to wavelet transform. Further, as shown in the following expression (2), the function g is obtained from the function h ', and the function g'for performing the inverse wavelet transform is obtained from the function h.

[0040]

[Table 1]

G ′ = (− 1)ⁿh g = (-1)ⁿh '... (2) In this way, the wavelet transform coefficient signals Wg0, Wg
When h0 is obtained, the wavelet transform coefficient signal Wg0,
For Wh0, pixels in the main scanning direction are thinned out every other pixel
The number of pixels in the main scanning direction is halved. Next, this picture
Wavelet transform coefficient signals Wg0 and Wh0 with thinned out elements
Filtering is performed in each sub-scanning direction by the functions g and h.
Wavelet transform coefficient signal WW₀, W
V₀, VW ₀And VV₀Get.

Next, the wavelet transform coefficient signal W
With respect to W ₀ , WV ₀ , VW ₀ and VV ₀ , the pixels in the sub-scanning direction are thinned out every other pixel, and the number of pixels in the sub-scanning direction is halved. As a result, each wavelet transform coefficient signal VV ₀ , WV ₀ , VW ₀ , WW
The number of pixels of ₀ is 1/4 of the number of pixels of the original image data Sorg. Next, filtering processing is performed by the functions g and h in the main scanning direction of the wavelet transform coefficient signal VV ₀ .

That is, the filtering processing for each column of pixels arranged in the main scanning direction by the functions g and h is performed while shifting each pixel in the sub scanning direction to obtain the wavelet transform coefficient signal of the wavelet transform coefficient signal VV _{0 in} the main scanning direction. Wg1 and Wh1 are obtained.

Here, the wavelet transform coefficient signal VV ₀
Since the number of pixels in both main and sub directions is half that of the original image data, the image resolution is half that of the original image data. Therefore, by filtering the wavelet transform coefficient signal VV ₀ with the functions g and h, the wavelet transform coefficient signal representing a frequency component lower than the frequency component represented by the wavelet transform coefficient signal VV ₀ among the frequency components of the original image data. Wg1, Wh1
Is required.

When the wavelet transform coefficient signals Wg1 and Wh1 are obtained in this way, the wavelet transform coefficient signals Wg1 and Wh1 are thinned out every other pixel in the main scanning direction, and the number of pixels in the main scanning direction is further reduced to 1. / 2 Then the wavelet transform factor signals Wg1, Wh1 function g in each of the sub-scanning direction, performs a filtering process by h, the wavelet transform factor signals WW _1, W
Obtain V ₁ , VW ₁ and VV ₁ .

Next, the wavelet transform coefficient signal W
For W ₁ , WV ₁ , VW ₁ , and VV ₁ , pixels in the sub-scanning direction are thinned out every other pixel, and the number of pixels in the sub-scanning direction is halved.
And perform the process. As a result, the number of pixels of each wavelet transform coefficient signal VV ₁ , WV ₁ , VW ₁ , WW ₁ becomes 1/16 of the number of pixels of the original image data Sorg.

Thereafter, in the same manner as described above, filtering processing is performed by the functions g and h in the main scanning direction of the wavelet transform coefficient signal VV ₁ from which pixels are thinned, and the main wavelet transform coefficient signal obtained is further processed. Pixels in the scanning direction are thinned out, and the wavelet transform coefficient signal obtained by thinning out the pixels is filtered in the sub-scanning direction by the functions g and h to obtain the wavelet transform coefficient signal W.
W ₂ , WV ₂ , VW ₂ and VV ₂ are obtained.

By repeating such wavelet transform N times, wavelet transform coefficient signals WW _{0 to} W
_{W N, WV 0 ~WV N,} VW 0 ~VW N, and VV _N
Get. Here, the wavelet transform factor signals obtained by the wavelet transform of the N-th WW _N, WV _N, V
W _N, VV _N are the original as compared with the image data is the number of pixels in the main sub each direction (1/2) because it is a ^N, each wavelet transform factor signal has a low enough frequency bands N is large, the original The data represents low frequency components of the frequency components of the image data.

Therefore, the wavelet transform coefficient signal WW _i (i = 0 to N, the same applies hereinafter) is used as the original image data Sor.
It represents a change in the frequency of g in both main and sub directions, and the larger i is, the lower the frequency of the signal becomes. The wavelet transform coefficient signal WV _i represents the change in the frequency of the image signal Sorg in the main scanning direction, and the larger i is, the lower the frequency signal becomes. Furthermore, the wavelet transform coefficient signal VW _i represents the change in frequency of the image signal Sorg in the sub-scanning direction, and the larger i is, the lower the frequency signal becomes.

FIG. 4 shows a wavelet transform coefficient signal for each of a plurality of frequency bands. FIG.
For convenience, the state up to the state in which the third wavelet transform is performed is represented by. In FIG. 4, the wavelet transform coefficient signal WW ₃ is obtained by reducing the original image to (1/2) ³ in each of the main and sub directions.

Next, the wavelet transform coefficient signal W of the smallest frequency band obtained by performing the wavelet transform N times is performed.
Histogram analysis is performed on W _N , and the importance of each density band in the image is obtained based on the result of this histogram analysis. FIG. 5 shows the wavelet transform coefficient signal W.
It is a figure showing the histogram of W _N. In the histogram shown in FIG. 5, the shaded area A is the most important area (in the case of the chest radiographic image shown in FIG. 7, the lung field).
Area B is the area other than the lung field and is the second most important area. The region C is a region of almost no importance corresponding to the sunken part directly irradiated with the radiation.

When the histogram analysis is thus performed and the importance is obtained, a quantization table corresponding to the importance is selected from a plurality of quantization tables having different quantization widths. It That is, a table is selected such that quantization is performed with a finer quantization width in the more important density band. Here, an example of the quantization table is shown in FIG. In the table shown in FIG. 6, Table 1 has the smallest quantization width, and Table 2 and Table 3 have coarser quantization widths in this order. And these three tables 1,
2 and 3 are selected according to the importance obtained from the result of the histogram analysis shown in FIG. That is, as shown in FIG. 5, for region A corresponding to the lung field, table 1 with the smallest quantization width, for region B, the table 2 with the second smallest quantization width, and for region C, Is selected so that the table 3 having a coarse quantization width is assigned. FIG. 7 shows the relationship between the selected table and the density band in the image. The numbers in FIG. 7 correspond to the numbers in the table.

In this way, the table according to the concentration band
Is selected, then the wavelet transform coefficients
Signal WV_i, VW_i, WW_iIs quantized about
You. Here, each wavelet transform coefficient signal WV_i, V
W_i, WW_iThe image represented by is a reduced version of the original image.
Therefore, the wavelet transform coefficient signal WW
_NFor each concentration band corresponding to
Bullet transform coefficient signal WV_i, VW_i, WW_iabout
Is also supported. Therefore each wave
Let transform coefficient signal WV_i, VW_i, WW_iQuantize
Table 1, 2 and 3 according to the concentration band
And quantize.

Here, when the data is quantized, the smaller the quantization width, the more the data can be compressed in a state closer to the original image, but the compression rate cannot be improved so much. Further, if the quantization width is made coarse, the compression rate can be improved, but there is a large error when the compressed data is restored, and there is more noise than the original image.

Therefore, in the present invention, the quantization width is coarse for the image data in the high frequency band that carries a lot of noise components, and the quantization width is fine for the image data in the low frequency band that carries the information of the main subject. In addition, for each of the wavelet transform coefficient signals WV _i , VW _i , and WW _i , not the same number of bits as a whole, but a table in which the quantization can be performed with a finer quantization width in an important portion is selected to improve the image quality. Since the image quality is not so important for the unimportant part, the table that performs the quantization with the coarse quantization width is selected to improve the compression ratio, and the compression ratio is improved while maintaining the image quality of the main part of the image as a whole. It is intended to improve.

After each wavelet transform coefficient signal is quantized as described above, compression processing is performed by performing the above-mentioned Huffman coding, predictive coding and the like. In addition, at the time of this encoding, the density band and the information of the table corresponding thereto are simultaneously encoded,
This is preferable because the decryption can be performed immediately.

The original image data Sorg encoded and compressed in this way is stored in a recording medium such as an optical disk, and stored and transported.

Next, a method of reconstructing compressed data will be described.

First, by decoding the compressed original image data by Huffman coding or predictive coding, the inverse quantization table is switched, and the respective wavelet transform coefficient signals WV _i , VW _i , Get WW _i .

Next, the wavelet transform coefficient signals WV _i , VW _i , WW obtained by the decoding are performed.
Inverse wavelet transform is applied to _i .

FIG. 8 is a diagram showing details of the inverse wavelet transform.

[0062] As shown in FIG. 8, first, the wavelet transform factor signals _{VV N, VW N, WV N} , the WW _N performs processing spacing of one pixel among pixels arranged in the sub-scanning direction (× in FIG. 2). Followed by different function h 'is the function h described above wavelet transform factor signals VV _N this spaced in the sub-function g mentioned above the wavelet transform factor signal VW _N in the sub-scanning direction
Filtering processing is performed by a function g ′ different from.
That is, the function g ', h' wavelet transform factor signals by VV _N, filtering processing for each pixel of a row aligned in the sub-scanning direction VW _N in the main scanning direction is performed while Shifts pixel by pixel, the wavelet transform factor signals VV _N , V
An inverse wavelet transform coefficient signal W _N ′ is obtained by doubling and adding the inverse wavelet transform coefficient signal W _N ′.

The function for performing the wavelet transform and the function for performing the inverse wavelet transform are different from each other for the following reason. Wavelet transform and inverse wavelet transform have the same function,
That is, it is difficult to design orthogonal functions, and it is necessary to loosen any of the conditions of orthogonality, continuity, function shortness, and symmetry. Therefore, a function that satisfies other conditions is selected by relaxing the orthogonality condition.

As described above, in the present embodiment, the functions h and g for performing the wavelet transform and the functions h'and g'for performing the inverse wavelet transform are biorthogonally different from each other. Thus, the wavelet transform factor signals VV _i, V
The original image data can be completely restored by inverse wavelet transforming W _i , WV _i , and WW _i with the functions h ′ and g ′.

On the other hand, in parallel with this, the wavelet transform coefficient signal WV _N in the sub-scanning direction is given by the function h ', and the wavelet transform coefficient signal WW _N is given in the sub-scanning direction by the function g'.
The performs filtering process, to obtain an inverse wavelet transform factor signals of the wavelet transform factor signals WV _N, WW _N, obtain the inverse wavelet transform factor signals WGN 'by adding to 2 times this.

Next, the inverse wavelet transform coefficient signal W
With respect to hN 'and WgN', a process of spacing one pixel between pixels arranged in the main scanning direction is performed. Then, the inverse wavelet transform coefficient signal WhN 'is filtered in the main scanning direction by the function h', and the inverse wavelet transform coefficient signal WgN 'is filtered in the main scanning direction by the function g', and the inverse wavelet of the wavelet transform coefficient signals WhN 'and WgN' is obtained. An inverse wavelet transform coefficient signal VVN _-1 'is obtained by obtaining a transform coefficient signal, doubling it, and adding it.

Next, the inverse wavelet transform coefficient signal VVN _-1 'and the wavelet transform coefficient signals VWN _-1 , W
1 between pixels arranged in the sub _- scanning direction for V _N-1 and WW _N- 1
Performs a process for spacing pixels. After that, the inverse wavelet transform coefficient signal VVN _-1 'is processed in the sub-scanning direction by the above-mentioned function h'.
_N-1 is filtered in the sub _- scanning direction by the function g'described above. That is, the function g ', h' wavelet transform factor signal VV _N-1 'according conducted while Shifts pixel by pixel filtering processing for each pixel of a row aligned in the sub-scanning direction of the VW _N-1 in the main scanning direction, wavelet transform factor signals VV _N-1 to obtain a ', to give the inverse wavelet transform factor signal VW _N-1, the inverse wavelet transform factor signals WhN-1 by adding to 2 times this'.

On the other hand, in parallel with this, the wavelet transform coefficient signal WVN _-1 is filtered in the sub _- scanning direction by the function h ', and the wavelet transform coefficient signal WW _N-1 is filtered in the sub _- scanning direction by the function g'. , to obtain a wavelet transform factor signals WV _N-1, the inverse wavelet transform factor signal WW _N-1, to obtain an inverse wavelet transform factor signals WGN-1 'by adding to 2 times this.

Next, the inverse wavelet transform coefficient signal W
1 between pixels lined up in the main scanning direction for hN-1 'and WgN-1'
Performs a process for spacing pixels. Thereafter, the inverse wavelet transform coefficient signal WhN-1 'is filtered in the main scanning direction by the function h', and the inverse wavelet transform coefficient signal WgN-1 'is filtered in the main scanning direction by the function g', and the wavelet transform coefficient signal WhN-1 'is obtained. , WGN-1 to obtain a 'give inverse wavelet transform factor signals, inverse wavelet transform factor signal VV _N-2 by adding to 2 times this'.

Thereafter, the inverse wavelet transform coefficient signal VV _i ′ (i = −1 to N) is sequentially created, and finally the inverse wavelet transform coefficient signal VV ₋₁ ′ is obtained. This final inverse wavelet transform coefficient signal VV ₋₁ ′ is the original image data S
Image data representing org.

The wavelet transform coefficient signal VV _-1 ′ thus obtained is sent to an image reproducing device (not shown) and used for reproducing a radiation image.

The reproducing device may be a display device such as a CRT or a recording device for performing optical scanning recording on the photosensitive film.

Here, in the reproduced image, there is a lot of noise in the part other than the lung field part, but there is no problem because it is not a part carrying important information.

In this way, the original image data Sorg is wavelet-transformed to obtain image data for each of a plurality of frequency bands, and the portion of the image data that carries important information is quantized with a fine quantization width. It is possible to improve the data compression rate while maintaining the image quality of the important portion by selecting the table to be performed and the quantization table with the coarse quantization width for the unimportant portion.

Further, in comparison with the above-mentioned JP-A-6-350989,
Since the entropy of the high frequency component becomes small, it is possible to perform data compression with a higher compression rate. For example,
Comparing the case where the same data as shown in FIG. 9 is compressed by the method of Japanese Patent Laid-Open No. 6-350989 and the method of this embodiment, many data values are smaller in this embodiment. Therefore, a higher compression rate can be realized. Further, even when the inverse quantization is performed, the data can be reproduced without missing the data in the highly important portion. It should be noted that in the present embodiment, since it is necessary to obtain the degree of importance again at the time of decoding, the calculation time for decoding is required. However, by associating the importance with the density-dependent table and encoding this table at the same time, the decoding operation can be performed at high speed.

In the above-described embodiment, the functions h and h'for performing the wavelet transform are shown in Table 1.
However, the present invention is not limited to this, and those shown in Tables 2 and 3 below may be used.

[0077]

[Table 2]

[0078]

[Table 3]

In addition to this, any function may be used as long as it can perform wavelet transformation, and for example, a function that is not symmetric but orthogonal but not symmetric may be used.

Further, as shown in Tables 1, 2, and 3, n
Not only the function symmetrical about the axis of = 0, but n = 0
The wavelet transform may be performed using a function that is asymmetric with respect to the axis of. When the wavelet transform is performed using the left-right asymmetric function as described above, the inverse wavelet transform is performed by using the function obtained by inverting the left-right function of the wavelet-transformed function with respect to the axis of n = 0. That is, with respect to the left-right asymmetric functions g and h, the functions g ′ and h ′ that perform the inverse wavelet transform are g [n] = g ′ [− n] h [n] = h ′ [− n] (3) However, [-n] represents left-right inversion.

It becomes

In the above embodiment, the image is converted into the multi-resolution image by the wavelet transform, but the present invention is not limited to this, and the image is converted by the above-mentioned Laplacian pyramid method or Fourier transform. The resolution may be converted into multiple resolutions.

Next, a second embodiment of the present invention will be described. FIG. 10 is a diagram showing the basic concept of the image data compression processing method according to the second embodiment of the present invention. FIG.
The description of the same configurations as those in FIG. 1 will be omitted, and only the configurations different from those in FIG. 1 will be described. 10 is different from FIG. 1 in that the differential processing 4'is performed on each image data 3 and the importance 5 of the differential value in the image is recognized based on the result of the differential processing. . This differential processing will be described below.

This differentiating process is performed on the wavelet transform coefficient signal WW _N as in the above-described embodiment, and the following equation (4) is applied to each pixel value of the wavelet transform coefficient signal WW _N. The processing is performed by.

Differential value = 4f (i, j) -f (i + 1, j) -f (i-1, j) -f (i, j + 1) -f (i, j-1) ( 4) And in this way the wavelet transform coefficient signal WW
_{After obtaining} the differential value of _N , the importance for each differential value is obtained. In the differential value calculated in this way, if the image is deteriorated due to data compression, this deterioration becomes very noticeable in the part corresponding to the character in the image where the differential value is uniquely large. It is preferable to perform the quantization with the smallest possible quantization width so as not to cause it. Further, even if the image is slightly deteriorated in the strong edge portion of the subject in the image, it does not matter. Therefore, based on the magnitude of the differential value calculated in this way, the above-mentioned tables 1, 2 and 3 are selected and assigned according to the importance in the image as shown in FIG. That is, the table 1 having the highest number of quantization bits is used for the portion corresponding to the character in the image having a specifically large differential value, and the table having the lowest number of quantization bits is used for the edge portion of the subject in the image. 3 and, for the rest, the second highest number of bits of quantization Table 2
Each table is selected so that

When the table is selected according to the differential value in this way, the wavelet transform coefficient signals WV _i , VW _i , W are then generated in the same manner as in the above-described embodiment.
Quantization is performed on W _i . Here, since the image represented by each of the wavelet transform coefficient signals WV _i , VW _i , and WW _i is a reduced version of the original image, the differential value of the wavelet transform coefficient signal WW _N is
Each wavelet transform coefficient signal WV _i , VW _i , WW _i
Is also supported. Therefore, when quantizing each of the wavelet transform coefficient signals WV _i , VW _i , WW _i , the tables 1, 2, 3 are assigned according to the differential values and the quantization is performed.

After each wavelet transform coefficient signal is quantized as described above, compression processing is performed by performing the above-mentioned Huffman coding, predictive coding and the like.

When reconstructing the data compressed in this way, the quantized data is subjected to differential processing again to obtain a differential value, and based on the differential value, tables 1, 2, 3 Decoding is performed by selecting an inverse quantization table corresponding to and performing inverse quantization. As described above, in the present embodiment, it is necessary to perform the differentiating process again on the quantized data, but since the differential operation is simple as shown in the above equation (4), the operation is performed. The processing load is not a problem.

In this way, the degree of importance in the image is obtained based on the differential value of the wavelet transform coefficient signal WW _N ,
Since the quantization table is selected according to this importance, compression can be performed while suppressing the deterioration of image quality for the part with a large differential value such as character information, and comparing the differential value. In a lighter shaded area or the like, compression can be performed with a high compression rate.

In the above-described embodiment, the embodiment in which the original image data representing the radiation image is compressed is described, but the image compression processing method according to the present invention can be applied to a normal image. Is.

Further, the original image data can be reconstructed by decoding the compressed image data in the same manner as in the above-mentioned embodiment and further applying the inverse wavelet transform.

[Brief description of drawings]

FIG. 1 is a diagram showing a basic concept of an image data compression processing method according to the present invention.

FIG. 2 is a diagram showing a method of reading image data used in the present invention.

FIG. 3 is a diagram showing details of a wavelet transform.

FIG. 4 is a diagram showing a wavelet transform coefficient signal.

FIG. 5 is a diagram showing a histogram of a wavelet transform coefficient signal WW _N.

FIG. 6 is a diagram showing a quantization table.

FIG. 7 is a diagram showing a radiographic image of the chest.

FIG. 8 is a diagram showing details of inverse wavelet transform.

FIG. 9 is a diagram showing a comparison between the present invention and the prior art.

FIG. 10 is a diagram showing a basic concept of another image data compression processing method according to the present invention.

FIG. 11 is a diagram showing a quantization table selected for each differential value.

FIG. 12 is a diagram showing a basic wavelet function used for wavelet transform.

FIG. 13 is a diagram for explaining a wavelet transform.

FIG. 14 is a diagram for explaining a Fourier transform.

[Explanation of symbols]

10 stimulable phosphor sheet h, h ', g, g ' function VV _i for performing a wavelet _{transform, VW i, WV i, WW} i (i = 1~n) wavelet transform factor signals

Claims (4)

[Claims] 1. An image data compression processing method for performing compression processing on original image data representing an image including a predetermined subject, wherein multiresolution conversion is performed on the original image data,
The original image data is decomposed into image data for each of a plurality of frequency bands, a histogram analysis is performed on each of the image data to recognize the importance of each density band in the image, and a plurality of predetermined quantum From the quantization table, a quantization table corresponding to the density band in the image is selected according to the degree of importance, the image data is quantized by the selected quantization table, and the quantized An image data compression processing method characterized in that each image data is encoded. 2. The image data compression according to claim 1, wherein the encoded image data is decoded, the decoded image data is inversely quantized, and inverse multiresolution conversion is performed. A method for reconstructing image data, comprising reconstructing the original image data compressed by a processing method. 3. An image data compression processing method for performing compression processing on original image data representing an image including a predetermined subject, wherein multiresolution conversion is performed on the original image data,
The original image data is decomposed into image data for each of a plurality of frequency bands, the differential processing is performed on each of the image data to recognize the importance of each differential value in the image, and a plurality of predetermined quantum From the quantization table, a quantization table corresponding to the differential value in the image is selected according to the degree of importance, the image data is quantized by the selected quantization table, and the quantized An image data compression processing method characterized in that each image data is encoded. 4. The image data compression according to claim 3, wherein the encoded image data is decoded, the decoded image data is inversely quantized, and inverse multiresolution conversion is performed. A method for reconstructing image data, comprising reconstructing the original image data compressed by a processing method.