JPH0714210B2 - Orthogonal transform coding method for image data - Google Patents

Description

Description: FIELD OF THE INVENTION The present invention relates to an image data encoding method for data compression, and more particularly to an image data encoding method using orthogonal transformation.

(Technical Background of the Invention and Prior Art) Since an image signal carrying a halftone image, such as a TV signal, has an enormous amount of information, its transmission requires a wide band transmission line. 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. In addition, recently, for example, it has been widely performed to record a halftone image on an optical disk or a magnetic disk,
In this case, image data compression is widely applied for the purpose of efficiently recording an image signal on a recording medium.

As one of such image data compression methods, a method using orthogonal transformation of image data is well known. In this method, the digital two-dimensional image data is divided into blocks each having an appropriate number of samples, and a numerical sequence consisting of sample values is orthogonally transformed for each block, and this transformation concentrates energy in a specific component. The large component of is encoded (quantized) by assigning a long code length, while the low energy component is roughly encoded with a short code length,
The number of codes per block is reduced. Examples of the orthogonal transform include a Fourier transform, a cosine transform, a Hadamard transform, a Karhunen-Love transform, and a Haar.
The ar) transformation and the like are often used, and the Hadamard transformation will be taken as an example to explain the method in more detail. First, as shown in FIG. 2, the block is formed by dividing digital two-dimensional image data into two pieces in a predetermined one-dimensional direction. Two sample values x in this block
When (0) and x (1) are shown in an orthogonal coordinate system, they have a high correlation as described above, and therefore x as shown in FIG.
There are many distributions near the straight line (1) = x (0). Therefore, this Cartesian coordinate system is set to 45 ° as shown in Fig. 3.
Transform to define a new y (0) -y (1) coordinate system.
In this coordinate system, y (0) represents the low frequency component of the original image data before conversion, and y (0) is x
(0), a value slightly larger than x (1) (about √2 times), while y indicating the high-frequency component of the original image data
(1) is distributed only in a very narrow range near the y (0) axis. So, for example, x (0), x (1) above
If a code length of 7 bits is required for each encoding of y, 0 or 7 bits are required for y (0), while y (1) has a code length of, for example, 4 bits. As a result, the code length per block is reduced, and image data compression is realized.

The quadratic orthogonal transformation that forms one block for each two image data has been described above. However, as the order is increased, the energy tends to concentrate on a specific component, and the effect of reducing the number of bits is increased. You can Generally, the above transformation can be performed by using an orthogonal function matrix. In the limit, if the eigenfunction of the target image is selected as the orthogonal function matrix, the transformed image becomes the eigenvalue matrix, and the matrix pair The original image can be represented only by the corner components. Further, in the above example, the image data is grouped into blocks only in the one-dimensional direction, but this block may be composed of several pieces of image data in the two-dimensional direction. A more significant bit number reduction effect can be obtained than in the case.

The transformation data obtained by the above-mentioned two-dimensional orthogonal transformation is the sequence (0) of the orthogonal functions used for transformation in each block.
(Number crossing) is arranged in order. Since this sequence corresponds to the spatial frequency, each conversion data is arranged in the order of frequency in the vertical and horizontal directions as shown in FIG. Therefore, a relatively long code length is assigned to the transform data (data on the upper left side in FIG. 4) that bears the low-frequency component (a long code length was assigned to y (0) in the above-described one-dimensional second-order orthogonal transform). The corresponding code length per block is reduced by assigning or truncating a relatively short code length to the converted data (data on the lower right side in FIG. 4) that carries the high frequency component.

The truncation of the conversion data described above is performed by so-called zone sampling as is well known. That is, the fourth
As shown in the figure, since the conversion data is arranged in the order of frequency in the vertical and horizontal directions in each block B, the conversion data y of the same frequency component is formed on the arc R centered on the conversion data y (1,1) of sequence 0. Will be lined up. Therefore, for example, by sampling the conversion data y in the zone Z shown by hatching in FIG. 4 and encoding only the sampled conversion data y, a significant data compression can be achieved.

However, since the conversion data distribution state in the block B is different for each image and for each block, if the zone Z is fixed, conversion data useful for image reproduction is truncated in a certain block. As a result, the image quality of the reproduced image deteriorates, and on the contrary, in some blocks, conversion data that is not so useful is also sampled, and the image data compression effect cannot be enhanced. In order to eliminate such a problem, conventionally, multiple sampling zone patterns are prepared, the appropriate zone pattern is selected after recognizing the conversion data distribution state of each block, and based on the selected pattern. It is also considered to perform zone sampling.
However, in such a method, the number of zone patterns that can be prepared is practically limited, so that it is naturally possible that zone sampling is performed based on a zone pattern that is not so appropriate.

(Object of the Invention) The present invention has been made in view of the above circumstances, and it is possible to maximize the image data compression effect for any block of image data without deteriorating the quality of the reproduced image. it can. An object of the present invention is to provide an orthogonal transform coding method for image data.

(Structure of the Invention) A first orthogonal transform coding method for image data according to the present invention is characterized in that a characteristic value that increases / decreases in response to an increase / decrease in absolute value of each piece of transform data in each block is changed from a high frequency component side to a low frequency component side. It is characterized in that the data is sequentially added toward the side, and in each block, only the conversion data on the lower frequency side than the conversion data when the added value reaches a predetermined value is zone-sampled and encoded. To do.

The second method of orthogonal transform coding of image data of the present invention is basically the same as the first method, and the transform on the lower frequency side than the transform data when the above-mentioned added value reaches a predetermined value. Although only the data is zone-sampled, when the absolute value of the converted data showing high frequency components is small overall, the area where the converted data is truncated (that is, not sampled) does not unnecessarily expand to the low frequency area. Specifically, the limit frequency is set in advance, and when the frequency when the above-mentioned added value reaches the predetermined value is lower than this limit frequency, the frequency on the lower frequency side than this limit frequency is set. Sample all the converted data,
On the contrary, when the frequency when the added value reaches the predetermined value is equal to or higher than this limit frequency, the converted data on the lower frequency side than the converted data when the added value reaches the predetermined value as above. Only the sampling frequency (that is, the limit frequency is compared with the frequency when the added value reaches a predetermined value, the higher frequency is selected, and only the conversion data on the lower frequency side than the selected frequency is selected. Is sampled), and the sampled conversion data is encoded.

Also, the third orthogonal transform coding method for image data according to the present invention is basically the same as the first method, and is on the lower frequency side than the transform data when the above-mentioned added value reaches a predetermined value. Although only the converted data is sampled, it prevents generation of a density step at the boundary between the blocks in the reproduced image (so-called block distortion), and specifically, the size of the sampling area in the adjacent blocks. When the difference in height exceeds a predetermined value, the high-frequency side limit of at least one of the sampling areas is corrected so that the difference is within the predetermined value.

The absolute value itself of the converted data, the squared value of the converted data, or the like can be used as the “characteristic value that increases or decreases according to the increase or decrease of the absolute value of the converted data” described above.

(Operation) When converted data, which is not so important for image reproduction, is relatively widely distributed in the high-frequency region on the lower right side in FIG. 4, the absolute value of the converted data is generally small, and therefore the characteristics described above are obtained. The converted data at which the added value of the values reaches the predetermined value exists on the lower frequency side. Therefore, in this case, according to the above method, the zone sampling area is set to be relatively narrow, and a large amount of less important converted data is discarded, so that the image data compression effect is enhanced.

On the other hand, when there are many converted data with large absolute values (these are important to the image reproduction to some extent) in the high frequency region shown in FIG. 4, the converted data where the added value of the characteristic values reaches a predetermined value is , On the higher frequency side.
Therefore, in this case, the zone sampling area is set to be relatively wide, and although the image data compression effect is reduced to some extent, conversion data useful for image reproduction is not truncated.

(Embodiment) Hereinafter, the present invention will be described in detail based on an embodiment shown in the drawings.

FIG. 1 schematically shows an apparatus for carrying out a first image data orthogonal transform coding method according to the present invention. The image data (original image data) x indicating a halftone image is first passed through the preprocessing circuit 10 and subjected to preprocessing such as smoothing for removing noise to improve data compression efficiency. The image data x subjected to this pre-processing is passed through the orthogonal transformation circuit 11 and first subjected to two-dimensional orthogonal transformation. This two-dimensional orthogonal transformation is, for example, the fifth
As shown in the figure, the halftone image G indicated by the image data x
This is performed for each rectangular block B of the sample number (pixel number) M × N in the inside. As the orthogonal transform, for example, the Hadamard transform described above is used. This Hadamard transform can be executed by a simpler transform circuit than other orthogonal transforms because its transform matrix consists of only +1 and -1. Further, as is well known, the two-dimensional orthogonal transform can be degenerated into the one-dimensional orthogonal transform. That is, the one-dimensional orthogonal transformation is applied to the image data regarding the M × N pixels in the two-dimensional block B in the vertical direction, and the obtained M × N transformed data is subjected to the one-dimensional orthogonal transformation in the horizontal direction. Two-dimensional direct exchange is carried out by applying. The order of conversion in the vertical direction and the conversion in the horizontal direction may be reversed.

Transformation data y obtained by the above two-dimensional orthogonal transformation
Is the function that is the basis of the above orthogonal transformation in each block B as shown in FIG. 4 (for example, in the Hadamard transformation,
Walsh function, in the Fourier transform, trigonometric functions, etc.) are arranged in the vertical and horizontal directions in the order of sequence. Since the sequence corresponds to the spatial frequency as described above, the converted data y is arranged in the block B in the order of the spatial frequency (that is, in the order of the density of the detail components of the image). In FIG. 4, the conversion data y (1,1) in the leftmost column of the top row corresponds to the sequence 0 (zero), and as is well known, this conversion data y (1,1)
1) shows the average image density in the block B.

The converted data y arranged in this way is sent to the encoding circuit 12 and encoded as shown in FIG. The encoding circuit 12 encodes each conversion data y in the block B with a code length (the number of bits) according to the allocation bit allocation table created as described later. For example, as shown in FIG. 6, the bit allocation table is one in which a unique number of bits is allocated for each sequence, and as described above, the conversion data y has energy concentrated in the low frequency component. A relatively long code length is given to low-frequency components with high energy,
On the other hand, by giving a relatively short code length to the high-frequency component having low energy, the number of bits required per block B is reduced and image data compression is achieved. Also, the sixth
As shown in the figure, by assigning 0 bits to the converted data y that carries a particularly high frequency component and discarding it, and sampling only the other converted data y (so-called zone sampling), the image data compression effect is further enhanced. To be enhanced.

However, here, if the zone sampling area is too wide, it becomes impossible to sufficiently enhance the image data compression effect, while if the zone sampling area is too narrow,
Many conversion data y useful for image reproduction are truncated. The characteristic part of the method of the present invention for properly setting the zone sampling region will be described below in accordance with the distribution state of the converted data y that differs for each image and for each block.

The bit allocation determining unit 20 connected to the encoding circuit 12 as shown in FIG. 1 receives the conversion data y arranged as described above, and calculates the absolute values of these conversion data y from the high frequency component side to the low frequency side. The components are sequentially added toward the component side. That is, this addition starts from the conversion data y (M, N) with the maximum sequence and is converted into the conversion data y as shown by the arrow H in FIG.
The absolute value of each conversion data y is added along a path along an arc R centered on (1,1). Then, when the added value reaches a predetermined value at a certain conversion data y, the bit allocation determination unit 20 allocates a code length of 1 bit or more only to the conversion data y on the lower frequency side than the conversion data y, and The conversion data y is assigned 0 (zero) bit. That is, referring to FIG. 4, assuming that the converted data y ₀ when the predetermined value is reached is the converted data
converted data y along the y ₀ as arc R and concentric arcs R _0, and assign a 0 bit on the transformed data y in the lower right side region than that. The number of bits to be assigned to each conversion data y to which a code length of 1 bit or more is assigned may be determined according to a distribution table stored in advance, or the actual conversion data y may be determined. The value of may be examined and determined accordingly. The flow of processing for determining the bit allocation in the bit allocation determining unit 20 described above is shown in FIG. 7 for easy understanding.

The information Q indicating the bit allocation determined as described above is sent to the encoding circuit 12, and the encoding circuit 12 encodes each conversion data y according to the bit allocation information. On this occasion,
As described above, the conversion data y to which 0 bits are assigned is truncated, and only the conversion data y to which a code length of 1 bit or more is given is sampled and encoded. Here, since the area of the converted data y to which 0 bit is assigned is determined as described above, the converted data y in the high frequency area in the block B is not so important for image reproduction (that is, the absolute value is small), and the converted data y is relatively wide. When distributed, the zone sampling area is set relatively narrow. Therefore, encoding less useful transformed data y is avoided and the number of bits required per block B is greatly reduced. On the other hand, in the block B, when the conversion data y, which is relatively important for image reproduction (that is, the absolute value is relatively large to some extent), is widely distributed up to the high frequency region, the zone sampling region is set relatively wide. Therefore, in this case, although the required number of bits per block B is larger than that in the above case, the converted data y useful for image reproduction is not truncated.

The image data g (y) and the position information of the arc R ₀ encoded as described above are recorded on a recording medium (image file) such as an optical disk or a magnetic disk in the recording / reproducing apparatus 13 as shown in FIG. Will be recorded. As described above, since the image data g (y) is significantly compressed with respect to the image data x, a large amount of images can be recorded on the recording medium such as an optical disk. At the time of image reproduction, the image data g (y) and the position information of the circular arc R ₀ are read from the recording medium and are decoded by the decoding circuit 14 into the converted data y. The transformed data y thus decoded is sent to the inverse transform circuit 15 and subjected to the inverse transform of the two-dimensional orthogonal transform. As a result, the original image data x is restored, this original image data x is sent to the image reproducing device 16, and the data x
The image carried by is reproduced.

Next, an embodiment of the second method of the present invention will be described.
This second method can be carried out by an apparatus having basically the same configuration as the apparatus shown in FIG. However, in this case, the bit allocation determination unit 20 is provided with a storage means as described later. Hereinafter, FIG. 8 showing a flow of bit allocation determination processing,
This bit allocation determination processing will be described with reference to FIG. The bit allocation determining unit 20 stores in memory means a predetermined limit frequency F'as shown in FIG. 10 and an arc R'centered on the converted data y (1,1) and passing through this limit frequency F '. There is. This arc R'defines the minimum zone sampling area that does not impair the quality of the reproduced image. That is, the zone sampling area is defined by this circular arc R '.
If the frequency is further narrowed to the low frequency side, the quality of the reproduced image may be impaired. It should be noted that such an arc R'can be obtained experimentally and empirically.

The bit allocation determining unit 20 determines the absolute value of each conversion data y as conversion data y (M,
N) is sequentially added, and converted data y ₀ when this added value reaches a predetermined value is obtained. Then bit allocation determining section 20 determines that the frequency F ₀ corresponding to the conversion data y _0, which one is higher in the critical frequency F '. Converted data y ₀
If the frequency F ₀ corresponding to is higher than the limit frequency F ′ (state as shown in FIG. 10), the bit allocation determination unit 20 performs conversion data y (1) as in the first method. ,
The area on the lower frequency side of the circular arc R ₀ passing through the converted data y ₀ centered on 1) is the zone sampling area. On the contrary, when the limit frequency F ′ is higher than the frequency F ₀ , that is, the conversion data y _{0 in} FIG.
The converted data y (1,
If it is close to 1), the bit allocation determination unit 20 sets the region on the lower frequency side of the circular arc R'as the zone sampling region. The bit allocation to each conversion data y in the zone sampling area thus determined is
The same procedure as in the first method is performed.

The encoding circuit 12 encodes the conversion data y in accordance with the bit allocation determined as described above. In the second method, the zone sampling area is determined as described above, so that the high frequency area is determined. Even if the converted data y having a small absolute value is widely distributed, the zone sampling area is not unnecessarily narrowed.
That is, the zone sampling area is an area on the lower frequency side than the circular arc R'at a minimum. By doing so, the converted data y useful for image reproduction will not be truncated.

Next, an embodiment of the third method of the present invention will be described.
This third method can also be implemented by an apparatus having basically the same configuration as the apparatus shown in FIG. However, in this case, the bit allocation determination unit 20 is provided with a storage unit that stores the zone sampling area of the adjacent block B. The bit allocation determination process will be described below with reference to FIGS. 9 and 11 showing the flow of the bit allocation determination process. The bit allocation determining unit 20 determines the zone sampling area and the bit allocation in each block B in the same manner as in the first method, but the difference between the sizes of the sampling areas in two adjacent blocks is set to a predetermined value. The correction processing to be contained within is performed at any time. That is, for example, as shown in FIG. 11, the zone sampling region of the block (B _n-1 ) subjected to the (n-1) th orthogonal transformation is a region on the lower frequency side than the circular arc R _n-1 , and The zone sampling area of the block B _n that has been subjected to the nth orthogonal transformation is an arc R _n.
It is assumed that the region is on the low frequency side. The bit allocation determination unit 20 determines the above-mentioned arc when determining the bit allocation of the block B _n.
Information indicating R _n-1 is stored in the storage means, and this arc R
Find the difference between the radius of _n-1 and the radius of arc R _n . Then, when the difference between the radii of the two exceeds the predetermined value r, the bit allocation determination unit 20 corrects the arc R _n so that the difference becomes r. FIG. 11, since the radius of the circular arc R _n is larger than the value obtained by adding r to the radius of the circular arc R _n-1, an arc of this value and the radius
Although it is shown as being modified to R _n ′, even _if the radius of the arc R _n is less than the value obtained by subtracting r from the radius of the arc R _n-1 , the radius of the arc R _n has this value. It is modified so that The bit allocation determination unit 20 uses the arc thus modified.
The zone sampling area is defined by R _n ′ or the arc R _n (when no correction is required), and the bit allocation in that area is determined. Then, the encoding circuit 12 encodes the converted data y according to this bit allocation. Further, the information indicating the arc R _n or R _n ′ is stored in the storage means in place of the information indicating the arc R _n-1 of the block B _n-1 , and then the block B _{n + 1 to} be orthogonally transformed. Is used to modify the zone sampling area of the if necessary.

As described above, by preventing the zone sampling areas of the adjacent blocks from being greatly separated from each other, it is possible to prevent the occurrence of so-called block distortion, which is a density step at the boundary of each block in the reproduced image. .

In the embodiment described above, the absolute value of the conversion data y is added in each block, and the added value is compared with a predetermined value. However, the characteristic value to be added in this way increases or decreases the absolute value. It is only necessary to increase / decrease correspondingly. Therefore, for example, the square value of the conversion data y or the like may be added. Further, as described above, the converted data y is more important for image reproduction on the lower frequency side, and therefore, as the above characteristic value, it is the reciprocal of the spatial frequency at which each converted data y indicates the amplitude. It is more preferable to use a weighted value. That is, by doing so, the converted data y having a small absolute value in the high frequency region can be obtained.
Can be prevented from being unnecessarily narrowed and the converted data y useful for image reproduction being truncated.

The predetermined value to be compared with the added value of the characteristic values may be set to an optimum value in advance, or may be a value obtained by accumulating all the characteristic values of the block B and multiplying the accumulated value by a predetermined ratio. Good, and furthermore, except for the characteristic value relating to the converted data y (1,1) of the sequence 0 which usually takes a particularly large value, all other characteristic values in the block B are accumulated,
A value obtained by multiplying this cumulative value by a predetermined ratio may be used.

(Effect of the Invention) As described in detail above, the orthogonal transform coding method for image data of the present invention is useful for image reproduction when performing zone sampling on the transform data obtained by orthogonal transforming image data block by block. After sampling all the data, the sampling area can be set particularly small. Therefore, according to the method of the present invention, the data compression rate can be significantly increased without deteriorating the image quality of the image reproduced after the data compression.

In particular, in the second method for orthogonal transform coding of image data of the present invention, the lower limit of the zone sampling area is set to prevent the area from becoming unnecessarily small. It is possible to prevent the quality of the reproduced image from being deteriorated due to the data being truncated.

Further, in the third method of orthogonal transform coding of image data of the present invention, since the sampling areas of adjacent blocks are not largely separated from each other, block distortion can be prevented from occurring in a reproduced image.

[Brief description of drawings]

FIG. 1 is a block diagram showing a schematic configuration of an apparatus for carrying out the method of the present invention, FIGS. 2 and 3 are explanatory diagrams for explaining orthogonal transform according to the present invention, and FIG. 4 is a first method of the present invention. 5 is an explanatory diagram for explaining zone sampling in FIG. 5, FIG. 5 is an explanatory diagram for explaining block division of an image according to the present invention, and FIG. 6 is an explanatory diagram for explaining a bit allocation table according to the present invention, 7, 8 and 9. FIG. 10 is a flow chart showing the flow of bit allocation determination processing in the first, second and third methods of the present invention, and FIGS. 10 and 11 show zone sampling in the second and third methods of the present invention, respectively. It is an explanatory view explaining. 11 ... Orthogonal transformation circuit, 12 ... Encoding circuit 20 ... Bit allocation determination unit F '... Limit frequency B, B _n-1 , B _n , B _{n + 1} ... Image data block R, R ₀ , R _n-1 , R _n , R _n ′ ... Circular arc centered on the converted data of sequence 0 r ... Radius difference of circular arc, x ... Original image data y ... Converted data y ₀ ... Where the added value of characteristic values reaches a predetermined value Conversion data g (y) ... Encoded image data

Claims (8)

[Claims] 1. Two-dimensional image data is subjected to orthogonal transformation for each block based on a predetermined orthogonal function, and after this transformation, the transformed data arranged in the sequence of the orthogonal functions in the block is received. In an orthogonal transform coding method of image data to be coded with a unique code length, a characteristic value that increases / decreases corresponding to an increase / decrease in the absolute value of each of the conversion data in the block is changed from the high frequency component side to the low frequency component side. Are sequentially added, and in each block, only the conversion data on the lower frequency side than the conversion data when this added value reaches a predetermined value is sampled, and the sampled conversion data is coded with a unique code length. An orthogonal transform coding method for image data, characterized by: 2. The orthogonal transform coding method for image data according to claim 1, wherein the characteristic value is an absolute value of the transformed data. 3. The orthogonal transform coding method for image data according to claim 1, wherein the characteristic value is a square value of the transformed data. 4. The characteristic value is weighted by the reciprocal of the spatial frequency at which the corresponding converted data indicates the amplitude, and the characteristic value is weighted by the reciprocal of the spatial frequency. An orthogonal transform encoding method for image data according to any one of items. 5. The predetermined value is a value obtained by multiplying a value obtained by accumulating all characteristic values in the block by a predetermined ratio, according to any one of claims 1 to 4.
An orthogonal transform encoding method for image data according to the item. 6. The predetermined value is a value obtained by multiplying a value obtained by accumulating all the characteristic values other than the characteristic value related to the conversion data of the sequence 0 (zero) in the block by a predetermined ratio. An orthogonal transform coding method for image data according to any one of claims 1 to 4. 7. Two-dimensional image data is subjected to an orthogonal transformation for each block based on a predetermined orthogonal function, and the transformed data which has undergone this transformation and is arranged in the sequence order of the orthogonal functions is given. In an orthogonal transform coding method of image data to be coded with a unique code length, a characteristic value that increases / decreases corresponding to an increase / decrease in the absolute value of each of the conversion data in the block is changed from the high frequency component side to the low frequency component side. In each block, the frequency corresponding to the converted data when the added value reaches a predetermined value is compared with the preset limit frequency, and the higher frequency is selected. Then, only the conversion data on the lower frequency side than the selected frequency is sampled, and this sampled conversion data is encoded with a unique code length. An orthogonal transform coding method for characteristic image data. 8. The two-dimensional image data is subjected to orthogonal transformation for each block based on a predetermined orthogonal function, and the transformed data which has undergone this transformation and is arranged in the sequence order of the orthogonal functions is given. In an orthogonal transform coding method of image data to be coded with a unique code length, a characteristic value that increases / decreases corresponding to an increase / decrease in the absolute value of each of the conversion data in the block is changed from the high frequency component side to the low frequency component side. Are sequentially added, and in each block, only the conversion data on the lower frequency side than the conversion data when this added value reaches a predetermined value is sampled, and the sampled conversion data is coded with a unique code length. If the difference in the size of the sampling areas in adjacent blocks exceeds a predetermined value, those samples are Orthogonal transform encoding method of the image data to which the difference of at least one of the high-frequency-side limit of the grayed area is equal to or be modified to be within a predetermined value.