JPH0723229A - Picture data compression processing method - Google Patents

Abstract

PURPOSE:To provide the picture data compression method in which picture data are compressed at a high compression ratio without deteriorating the picture quality of an original picture. CONSTITUTION:Wavelet transformation 2 is applied to original picture data 1 representing an original picture to obtain picture data 3 for plural frequency bands. Then processing 4 making non-linked components of the picture data 3 zero is executed in picture data 3 at a higher frequency band than a predetermined frequency band of the picture data 3. Then all the picture data 3 to which making 0 processing 4 is made and all the picture data 3 to which no making 0 processing 4 is made are coded in the step 5.

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 wavelet transform.

[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 large 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. Stored, transmitted, etc. above,
At the time of image reproduction, a method is adopted in which the compressed image data (compressed image data) is subjected to decoding processing and decompressed, and a visible image is reproduced based on the decompressed image data (decompressed image data). Has been done.

As one of image data compression methods,
A method utilizing vector quantization is known. 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. Is represented by a vector consisting of
Assuming that you create a codebook prepared as above, 1
The data amount per block is the data amount for vector identification, that is, 8 bits, and the data amount is 8 / (8 ×
64) = 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 reconstructed data for each block,
The original image is reproduced by using the reconstructed data.

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 application has proposed an image data compression method by interpolation coding, which is a combination of the method by predictive coding and the method by quantization described above (Japanese Patent Application 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 application has proposed an image data compression method capable of achieving a higher compression rate while maintaining higher image quality by combining the above-described interpolation coding and the image data number reduction process (special feature). Kaihei 2-280462).

On the other hand, a method called wavelet transform has been proposed as a method for processing the above-mentioned image data.

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. 7 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 FIG. 8, the signal Sorg
Is wavelet-transformed and the inverse wavelet transform is performed for each frequency band, and the signal Sorg is Fourier-transformed as shown in FIG. 9 and the signal is inverse Fourier-transformed for each frequency band. Compared with the conversion, it is possible to obtain the frequency signal in the frequency band corresponding to the vibration of the original signal Sorg. That is, in the Fourier transform, the frequency band 7 corresponding to the part B of the original signal Sorg
While the vibration is generated in the portion B'of, the wavelet transform does not generate the vibration in the portion A'of the frequency band W7 corresponding to the portion A of the original signal Sorg like the original signal. Become.

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 transform to convert the original image data into image data having a plurality of frequency bands, and each image data has a high noise component. The image data in the frequency band has a small number of bits, and the image data in the low frequency band that carries the main subject has a large number of bits and is subjected to the aforementioned vector quantization to compress the original image data. is there. 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.

[0019]

However, in the method of compressing image data using the above-mentioned wavelet transform, if the compression rate is further improved,
There is a possibility that the image quality of the original image may be deteriorated, and there is a limit to increase the compression rate of the image.

In view of the above circumstances, it is an object of the present invention to provide an image data compression processing method capable of compressing image data at a high compression rate without degrading the image quality of the original image.

[0021]

An image data compression processing method according to the present invention is an image data compression processing method for compressing original image data representing an image including a predetermined subject, wherein the original image data is wavelet transformed. By performing the above, the original image data is decomposed into image data for each of a plurality of frequency bands, and among the image data for each of the plurality of frequency bands, the value of the unconnected component of the image data in the image data of a predetermined frequency band. Is performed, and all of the image data that has been subjected to the processing of 0 and all the image data that has not been subjected to the processing of 0 is encoded. .

Here, the non-connected component means an isolated point, that is, a pixel having a value of 0 when there is a pixel having only one value.

Further, the image data reconstruction method according to the present invention is a method for reconstructing compressed image data obtained by the image data compression processing method according to the present invention.
The original image data is reconstructed by inversely encoding each of the encoded image data and applying an inverse wavelet transform to each of the inversely encoded image data.

[0024]

In the image data for each of a plurality of frequency bands obtained by wavelet transforming the image data, the position of the pixel carrying the image data of the noise component becomes an isolated point, that is, a non-connected component. However, the removal of this does not significantly affect the image quality. Also, when compressing image data,
The more the image data value is 0, the more the number of bits for encoding can be reduced, so that the compression rate can be improved. The present invention has been made paying attention to this point.

That is, the image data compression processing method according to the present invention performs processing for setting the non-connected component of the image data to 0 in a predetermined frequency band among the image data decomposed into a plurality of frequency bands by the wavelet transform. It was done like this.

As described above, by performing the processing for setting the non-connected components to 0, unnecessary information such as image noise can be removed, and the number of bits required for encoding these unnecessary information. Can be reduced or need not be encoded. This can improve the compression rate when compressing the image data.

Further, since the processing for setting the non-connected component to 0 is performed, the processing for setting the value of the image data of the pixel carrying useful information to 0 is not performed, and useful information may be lost. Absent.

Further, since unnecessary information such as image noise can be removed, the quality of the reproduced image can be improved.

The image data reconstruction method according to the present invention is such that the image data compressed by the image data compression processing method according to the present invention is inversely encoded, that is, decoded, and the inversely decoded image data is inversely encoded. Since the wavelet transform is applied, it is possible to reproduce the original image while maintaining the image quality of an important part of each part of the image.

[0030]

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

FIG. 1 is a diagram showing the basic concept of a first embodiment of an image data compression processing method according to the present invention. As shown in FIG. 1, in the embodiment of the image data compression processing method according to the present invention, original image data 1 representing an original image is subjected to wavelet transform 2 to obtain image data 3 for each of a plurality of frequency bands. Next, in the image data 3 in the frequency band higher than the predetermined frequency band among the image data 3, the process 4 for setting the value of the non-connected component of the image data to 0 is performed for each frequency band. Then, the coding 5 is performed on all the image data 3 for which the processing 4 for setting 0 is performed and for all the image data 3 for which the processing 4 for setting 0 is not performed.

The details of the embodiments of the present invention will be described below.

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

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

FIG. 3 is a diagram showing details of the wavelet transform for the original image data Sorg.

In the present embodiment, the orthogonal wavelet transform in which each coefficient of the wavelet transform is orthogonal is performed, which is described in the above-mentioned document of Marc Antonini et al.

As shown in FIG. 3, the filtering process is performed in the main scanning direction of the original image data Sorg by the function g and the function 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 each 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.

[0039]

[Table 1]

G ′ = (− 1) ⁿ h g = (− 1) ⁿ h ′ (2) In this way, the wavelet transform coefficient signals Wg 0, W
When h0 is obtained, the wavelet transform coefficient signal Wg0,
For Wh0, pixels in the main scanning direction are thinned out every other pixel, and the number of pixels in the main scanning direction is halved. Then, the wavelet transform coefficient signals Wg0 and Wh0 from which this pixel is thinned out.
Filtering processing is performed in each sub-scanning direction by the functions g and h, and wavelet transform coefficient signals WW ₀ and W
Obtain V ₀ , VW ₀ and VV ₀ .

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 ₀ and h _{0 is} performed while shifting the pixels one pixel at a time in the sub-scanning direction to obtain the wavelet transform coefficient of the wavelet transform coefficient signal VV _{0 in} the main scanning direction. The signals 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 frequency band of the image 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
To get Here, the wavelet transform factor signals obtained by the wavelet transform of the N-th WW _N, WV _N, V
Since W _N and VV _N each have (1/2) ^N pixels in both the main and sub directions as compared with the original image data, the larger the N of each wavelet transform coefficient signal, the lower the frequency band,
It becomes the data representing the low frequency component of the frequency component of the original 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 is a diagram showing the wavelet transform coefficient signal for each of a plurality of frequency bands. Note that, in FIG. 4, for convenience, the state up to the third wavelet transform is shown. In FIG. 4, the wavelet transform coefficient signal WW ₃ is the original image reduced to (1/2) ³ in each of the main and sub directions.

Next, each wavelet transform coefficient signal W
A process of setting the value of the non-connected component to 0 in the coefficient signal of a predetermined frequency band among the coefficient signals of V _i , VW _i , WW _i , and VV _i is performed.

For example, in the coefficient signal of a certain frequency band shown in FIG. 5, for a pixel w (i, j), pixels a (w (i, j-1)) and b (w in the vicinity of four points around it. (I + 1
, J)), c (w (i, j + 1)), d (w (i-1, j)) has a value of 0, the value of the pixel w (i, j) is set to 0. The processing is performed.

This processing is performed for all wavelet transform coefficient signals W in the frequency band higher than the low frequency band in which necessary information in the coefficient signal is not missing.
_{V i, VW i, WW i} , carried out on VV _i.

Then, the wavelet transform coefficient signals WV _i , VW _i , WW _i , and VV _i thus processed are subjected to the above-described Huffman coding, predictive coding, etc. to perform compression processing.

At this time, the value of the non-connected component, which is considered to be noise or the like, in the wavelet transform coefficient signal is set to 0, so that it is possible to reduce the number of bits for quantization by the amount of noise or the like.

Therefore, unnecessary information, such as noise, is removed from each of the wavelet transform coefficient signals WV _i , VW _i , WW _i , and VV _i to reduce the number of bits at the time of encoding, and necessary. In the portion having information, it is possible to improve the compression rate while maintaining a certain level of image quality depending on the number of bits used for normal encoding.

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. The encoding is described in JP-A-61-90531, JP-A-62-247676, JP-A-63-41714 and JP-A-1-01341. Therefore, detailed description is omitted here.

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

First, the compressed original image data is decoded to obtain the respective wavelet transform coefficient signals WV _i , VW _i , WW _i , VV _i . The details of this decryption are described in the above-mentioned Japanese Patent Laid-Open No. 61-90531, and the detailed description thereof is omitted here.

Next, the wavelet transform coefficient signals WV _i , VW _i , WW obtained by decoding are performed.
_i, performs inverse wavelet transform for VV _i.

FIG. 6 is a diagram showing details of the inverse wavelet transform.

[0061] As shown in FIG. 6, 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 , VW
_The inverse wavelet transform coefficient signal W is obtained by doubling and adding _N inverse wavelet transform coefficient signals.
get hN ′.

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 made bi-orthogonal different from each other. Therefore, the wavelet transform coefficient signals VV _i , VW _i , W
By inverse wavelet transforming V _i and WW _i with the functions h ′ and g ′, the original image data can be completely restored.

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 filtering processing is performed for each row of pixels of the wavelet transform coefficient signals VVN _-1 ', VWN _-1 by the functions g', h ', which are arranged in the sub _- scanning direction, while shifting each pixel in the main-scanning direction. 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 WV _N-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. Subsequent inverse wavelet transform factor signals WhN-1 'function in the main scanning direction h', 'functions g ₀ in the main scanning direction' inverse wavelet transform factor signals WGN-1 filtering process by the wavelet transform factor signals WhN-1 ', WGN-1' give the inverse wavelet transform factor signals of, obtaining the 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 is used for reproducing a radiation image.

This 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 above-described embodiment of the image data compression processing method according to the present invention, as processing for setting the non-connected component to 0, the pixels a, b, and 4 in the vicinity of four points of the pixel w (i, j) in FIG. Pixel w (i, j) when the values of c and d are 0
Is set to 0, but pixels a,
Pixel w when the values of b, c, d, e, f, g, and h are 0
(I, j) may be set to 0. Further, the wavelet transform coefficient signal WW _i indicates the change in frequency of the original image data Sorg in both main and sub directions, and the wavelet transform coefficient signal WV _i is the wavelet transform coefficient signal VW _{i in} the main scanning direction of the original image data Sorg. Represents the change in frequency in the sub-scanning direction, therefore, for the wavelet transform coefficient signal WW _i , the pixels a, b, c, d near the four points of the pixel w (i, j) are Regarding the signal WV _i , the pixels b and d near two points in the sub-scanning direction and the wavelet transform coefficient signal V
As for W _i , pixels a and c near two points in the main scanning direction may be used, and anisotropy may be imparted by the coefficient signal to make the non-connected component zero.

Further, the processing for setting the non-connected component to 0 may be performed until it converges. Further, as long as the processing for setting the non-connected component to 0, any processing is performed without being limited to the above processing. You can do it.

In the above-described embodiment of the image data compression processing method according to the present invention, the function h for performing wavelet transformation shown in Table 1 is used, but the function h is not limited to this. You may use what is shown in Table 2 and Table 3 shown.

[0075]

[Table 2]

[0076]

[Table 3]

In addition to this, any function may be used as long as it is a function capable of performing wavelet transformation, and for example, a function which is not symmetric but orthogonal but not symmetrical 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

Further, 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. .

For example, an example of compressing an image of a 35 mm negative film in which a person or the like is recorded as a main subject will be described. First, the negative film is read by a digital scanner to obtain image data representing this image, and the image data The wavelet transform is performed by performing the filtering process with the functions g and h as described above. Then, the wavelet transform coefficient signal obtained by performing the wavelet transform is subjected to processing for setting the non-connected component to 0, as in the above-described embodiment of the present invention.

Next, the image data is compressed by encoding the wavelet transform coefficient signal which has been processed to be 0.

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.

By performing the compression processing in this way,
The number of bits for encoding can be reduced by the amount of data set to 0, and the data compression rate can be improved.

Further, in the above-mentioned embodiment, the example in which the biorthogonal wavelet transform is used as the wavelet transform has been shown, but the present invention is not limited to this, and the coefficients divided into a plurality of frequency bands by the orthogonal wavelet transform are shown. A signal may be used.

Further, in the above-described embodiment, the processing for setting the non-connected component to 0 is performed based on the wavelet transform coefficient signal in the predetermined frequency band, but the processing is not particularly limited to this and all It is also possible to perform processing for setting the non-connected component of the data in the frequency band of 0 to 0. That is, in the above-described embodiment of the present invention, the processing of setting the non-connected component to 0 may be performed on the data in all the frequency bands from the lowest frequency band to the highest frequency band. .

[Brief description of drawings]

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

FIG. 6 is a diagram showing details of inverse wavelet transform.

FIG. 7 is a diagram showing a basic wavelet function used for wavelet transform.

FIG. 8 is a diagram for explaining a wavelet transform.

FIG. 9 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

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Office reference number FI technical display location H04N 7/30

Claims (2)

[Claims] 1. An image data compression processing method for performing compression processing on original image data representing an image including a predetermined subject, wherein the original image data is subjected to wavelet transformation to obtain the original image data for each of a plurality of frequency bands. Of the image data for each of the plurality of frequency bands, the process of setting the value of the non-connected component of the image data to 0 in the image data of the predetermined frequency band, and the process of setting the value to 0 are performed. An image data compression processing method, characterized in that all the image data that has been executed and all the image data that has not been processed to be 0 are encoded. 2. The image data compression processing method according to claim 1, wherein the encoded image data is inversely encoded, and inversely wavelet transform is applied to the inversely encoded image data. A method for reconstructing image data, comprising reconstructing the original image data that has been created.