JPH09214967A - Image data compression processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce the cost of a device by unnecessitating a mass memory for processing concerning an image data compression processing method for compressing source image data by performing multiplex resolution conversion to the source image data. SOLUTION: Source image data expressing a source image are divided 2 into plural blocks, wavelet transformation 3 is performed to block source image data in this block, and plural pieces of image data 4 are provided for each frequency band. Next, respective pieces of image data 4 are quantized 5. Afterwards, respective pieces of image data 4, to which the quantization 5 is performed, are encoded 6. In this case, when performing multiplex resolution conversion to block source image data inside a certain block, the source image data inside a block adjacent to this block are used as well. Thus, the phenomenon of return is not generated at the boundary of the adjacent block, the boundary of the block is continued even when the compressed image data are decoded, and an artifact can be prevented from being generated at the boundary of the block.

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 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 processing methods, conventionally, when image data is stored or transmitted, the image data is compressed by predictive coding to reduce the amount of data. The compressed image data (compressed image data) is subjected to decoding processing and expanded when the image is played back, and then stored, transmitted, etc., and based on the expanded image data (expanded image data). A method of reproducing a visible image is 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 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 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 higher image quality by combining the above-described interpolation coding and the image data number reduction processing ( JP-A-2-280462).

On the other hand, as a method for processing the above-described 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 are wavelet conversion, Laplacian pyramid, Fourier conversion and the like.

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. 8 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. 9, the signal Sorg
Is wavelet transformed and inverse wavelet transformed for each frequency band, and the signal Sorg is Fourier transformed as shown in FIG. 10 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 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 masking, the image is sub-sampled and the number of pixels is thinned to halve it to obtain a blurred image having a size of 1/4 of the original image, and the sampled pixel of this blurred image has a value of 0. 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, it is necessary to perform compression by vector quantization. Therefore, if the compression rate is further improved, the image quality of the original image is improved. However, there is a limit to how high the image compression rate can be. On the other hand, in the case of quantizing image data, if the number of bits for quantizing is increased, the data compression rate will decrease, but since it can be compressed in a state closer to the original image, the reconstructed image Image quality is less degraded. On the other hand, if the number of bits is lowered, the error when the compressed image data is restored is large, and this error appears as noise in the image when the image is restored, so the image quality of the reconstructed image deteriorates. Is large, but the code at the time of encoding becomes short, so that the data compression rate can be improved.

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 processing method has been proposed in which a portion having a high degree of quantization is quantized with a high number of bits, and a portion of low importance is quantized with a lower number of bits as compared with a portion having a high degree of importance (Japanese Patent Laid-Open Publication No. 6-58242). -350989). 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.

[0023]

However, in the method of compressing image data by using the multi-resolution conversion such as the above-mentioned wavelet conversion, if the entire image is converted and processed at the same time, It requires a large amount of memory, which increases the cost of the device for performing the compression process.

An object of the present invention is to provide an image data compression processing method capable of performing multi-resolution conversion on an image and performing image data compression processing without using a large capacity memory. Is.

[0025]

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 is divided into a plurality of blocks. By dividing the block original image data for each of the divided blocks, and performing multi-resolution conversion on the block original image data for each block, the block original image data for each of the plurality of frequency bands is obtained. It is characterized in that the image data is decomposed, the image data is quantized for each block, and the quantized image data is encoded.

In the image data compression processing method, when the block image data in one block is subjected to the multi-resolution conversion, the original image data of the block and the other blocks adjacent to the block are also processed. It is preferable to obtain the image data of the one block by performing the multi-resolution conversion on the block original image data around the one block.

[0027]

The image data compression processing method according to the present invention divides the original image into a plurality of blocks when performing the multi-resolution conversion on the original image data to perform the image data compression processing, and for each block. Multi-resolution conversion, quantization, and encoding are performed. Therefore, the memory for processing needs only to have a capacity capable of processing the data in the block, and as a result, a large-capacity memory is unnecessary and the cost of the device can be reduced.

When the original image is divided into blocks and the multi-resolution conversion is performed for each block, the multi-resolution conversion is folded back at the boundary between the blocks, and when the compressed image data is composited, Since the boundary becomes discontinuous and an artifact is generated at this boundary, it is not preferable for observing the image. Therefore, when performing the multi-resolution conversion, by performing the multi-resolution conversion on the block original image data of one block and the block original image data around the one block in another block adjacent to this block, Wrapping does not occur at the boundaries between blocks, and even if the compressed image data is combined, the boundaries of the blocks are continuous and it is possible to prevent the occurrence of artifacts at the boundaries of the blocks.

[0029]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is a diagram showing the basic concept of the image data compression processing method according to the present invention. As shown in FIG. 1, the image data compression processing method according to the present invention performs division 2 of original image data 1 representing an original image into a plurality of blocks, and the block original image data in the divided blocks is divided into blocks. Wavelet transform 3 is applied to each block to obtain image data 4 for each of a plurality of frequency bands. Next, each image data 4
Quantization 5 is performed on the image data 4 and encoding 6 is performed on each image data 4 that has been quantized 5.

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

The present embodiment is applicable to 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 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 10.
On the other hand, 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).

Next, the original image data Sorg is divided into a plurality of blocks. For example, as shown in FIG. 3, the original image data is divided into 9 blocks.

Next, for each block, the wavelet transform is performed on the block original image data Sorg 'in the block.

FIG. 4 is a diagram showing the details of the wavelet transform for the block 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. 4, filtering processing is performed in the main scanning direction of the block original image data Sorg 'using the functions g and h obtained from the basic wavelet function. That is, the filtering processing for each row 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, and the wavelet transform coefficient signal of the block original image data Sorg 'in the main scanning direction is obtained. Wg0 and Wh0 are obtained.

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) Here, if the filtering process by the functions g and h is performed for each block, the compressed image data is combined. When turned into a block, the boundary of the block becomes discontinuous due to the folding back of the filtering, and an artifact is generated at this boundary, which is not preferable for observing the image. Therefore, in the present embodiment, the filtering process is performed as follows. That is, when the block original image data Sorg 'in the block A shown in FIG. 3 is filtered by the functions g and h, the block original image data adjacent to the block A in the eight blocks around the block A are simultaneously filtered. It is something to process. That is, the filtering process is performed on the data in the area B shown in FIG. For example, when the mask size of the filter by the functions g and h is 9, and the first wavelet transform is performed, as shown in FIG.
Of the block original image data in the block adjacent to the block A, the block original image data for four pixels adjacent to the block A is simultaneously filtered with the block original image data in the block A. In the case where the filter mask size is 7 and the second-stage wavelet transform is performed, as shown in FIG. 5B, of the block original image data in the block adjacent to the block A, 9 pixels adjacent to the block A are included. The block original image data is filtered at the same time as the block original image data in the block A.

As described above, when the wavelet transform is applied to the block original image data Sorg 'in a certain block, the block original image data in the block adjacent to this block is also used, whereby the boundary between the adjacent blocks is The phenomenon of turning back does not occur at
Even if the compressed image data is combined, the block boundaries become continuous, and as a result, it is possible to prevent the occurrence of artifacts at the block boundaries.

When the wavelet transform coefficient signals Wg0 and Wh0 are obtained in this way, pixels of the wavelet transform coefficient signals Wg0 and Wh0 in the main scanning direction are thinned out every other pixel in the same manner. Number 1 /
Set to 2. Then, the wavelet transform factor signals Wg0 this pixel is thinned out, Wh0 function g in each of the sub-scanning direction, performs a filtering process by h, obtaining a wavelet transform factor signals WW _0, WV _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} the number of pixels of the block original image data Sorg ′.
It becomes 1/4. Then, the wavelet transform coefficient signal VV
Filtering processing is performed in the main scanning direction of _{0 by} the functions g and h.

That is, the filtering processing for each column of pixels lined up 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
Is the block original image data S with the number of pixels in both main and sub directions.
Since it is half of org ', the resolution of the image is half that of the original image data. Therefore, the block original image data S is obtained by filtering the wavelet transform coefficient signal VV ₀ with the functions g and h.
wavelet transform factor signals Wg1 than the frequency components represented by the wavelet transform factor signal VV ₀ among the frequency components of the org 'represents the low frequency components, Wh1 are obtained.

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 block 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
W _N, VV _N, since the number of pixels in the main sub each direction as compared to the block original image data Sorg 'is in the (1/2) ^N,
Each wavelet transform coefficient signal has a lower frequency band as N increases, and becomes data representing a low frequency component of the frequency components of the block original image data Sorg '.

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

FIG. 6 is a diagram showing the wavelet transform coefficient signal for each of a plurality of frequency bands. Note that, in FIG. 6, for convenience, the state up to the third wavelet transform is shown. In FIG. 6, the wavelet transform coefficient signal WW ₃ is (1/1)
2) It has been reduced to ³ .

Next, the wavelet transform coefficient signal WV
Quantization is performed on _i , VW _i , and WW _i . Here, when quantizing the data, the higher the number of bits, the more the data can be compressed in a state closer to the original image, but the compression rate cannot be improved so much. Also, the compression rate can be improved by reducing the number of bits,
There is a large error when decompressing the compressed data, and there is more noise than the original image.

Therefore, in the present invention, a small number of bits are assigned to the image data in the high frequency band that carries a large amount of noise components, and a large number of bits are assigned to the image data in the low frequency band that carries the information of the main subject. The wavelet transform coefficient signals WV _i , VW _i , and WW _i do not have the same number of bits as a whole, but the image quality is maintained by increasing the number of bits in more important portions, and the image quality in less important portions does not matter so much. Therefore, it is preferable to reduce the number of bits to improve the compression rate, and to improve the compression rate while maintaining the image quality of the main part of the image as a whole.

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

After the quantization is completed for one block, the block original image data S in another block
The wavelet transform, the quantization and the coding are sequentially applied to org ', and finally all the blocks are processed.

The block original image data Sorg 'for each block encoded and compressed in this way is stored in a recording medium such as an optical disk, and stored and transferred.

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

First, the block original image data Sorg 'compressed for each block is decoded by Huffman coding or predictive coding to obtain the above-mentioned wavelet transform coefficient signals WV _i , VW _i , WW. get _i

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

FIG. 7 is a diagram showing details of the inverse wavelet transform.

[0061] As shown in FIG. 7, each 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 bi-orthogonal 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 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. 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 ′ becomes image data representing the block original image data Sorg ′ for each block.

Then, the above-described decoding and inverse wavelet transform are applied to all blocks to obtain the inverse wavelet transform coefficient signal VV _-1 ′ for each block. Then, the inverse wavelet transform coefficient signal VV- ₁ 'is synthesized to restore the original image data Sorg.

The original image data S restored in this way
The org is sent to an image reproducing device (not shown) to be used for reproducing the 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.

As described above, the image data compression processing method according to the present invention divides the original image into a plurality of blocks, and performs multi-resolution conversion, quantization, and coding on each of the divided blocks. . For this reason, a large-capacity memory for processing is unnecessary, and the cost of the device can be reduced.

Further, when the original image is divided into blocks and the multi-resolution conversion is performed for each block, the multi-resolution conversion is folded back at the boundary between the blocks, and when the compressed image data is composited, Since the boundary becomes discontinuous and an artifact is generated at this boundary, it is not preferable for observing the image. Therefore, when performing the multi-resolution conversion, by performing the multi-resolution conversion on the block original image data of one block and the block original image data around the one block in another block adjacent to this one block, Even if the compressed image data is combined, the block boundaries become continuous, and it is possible to prevent the occurrence of artifacts at the block boundaries.

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

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]

Further, any function other than the above 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 =
The wavelet transform may be performed not only by using a function that is symmetric about the axis of 0 but also by using a function that is asymmetric about the axis of n = 0. 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, the left-right asymmetric function g,
a function g ′, which performs an inverse wavelet transform on h,
h'is g [n] = g '[-n] h [n] = h' [-n] (3) where [-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. Is.

For example, a description will be given of an embodiment in which a 35 mm negative film image in which a person or the like is recorded as a main subject is compressed. First, the negative film is read by a digital scanner, image data representing this image is obtained, and this image is obtained. The data is divided into a plurality of blocks, and each divided block is subjected to a filtering process by the functions g and h as described above to perform the wavelet transform.
Next, the wavelet transform coefficient signal obtained by performing the wavelet transform is quantized, and then encoded to compress the image data.

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

[Brief description of the 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 a state in which an original image is divided.

FIG. 4 is a diagram showing details of wavelet transform.

FIG. 5 is a diagram for explaining a wavelet transform using image data of adjacent blocks.

FIG. 6 is a diagram showing a wavelet transform coefficient signal.

FIG. 7 is a diagram showing details of inverse wavelet transform.

FIG. 8 is a diagram showing a basic wavelet function used for wavelet transform.

FIG. 9 is a diagram for explaining a wavelet transform.

FIG. 10 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 (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 is divided into a plurality of blocks, and the block original image data for each of the divided blocks. By performing multi-resolution conversion on the block original image data for each block, the block original image data is decomposed into image data for each of a plurality of frequency bands, and the image data for each block is obtained. Is quantized, and each of the quantized image data is encoded. 2. When the block original image data in one block is subjected to multi-resolution conversion, the block original image data of the block and block originals around the block in another block adjacent to the one block By applying the multi-resolution conversion to the image data,
The image data compression processing method according to claim 1, wherein image data of the one block is obtained.