JPH01276980A - Orthogonal transform coding system for picture data - Google Patents

Abstract

PURPOSE:To compress picture data at a high compressing ratio without deteriorating the picture quality by calculating and coding the mean value of orthogonally transforming data at every block of a low-frequency area and performing run-length coding on a high-frequency area after disintegrating the area into bit planes. CONSTITUTION:Picture data are orthogonally transformed into a spatial frequency system of 16X16 and DCT coefficients are found. Then the hatched area of the area pattern having the largest sum total value is selected as a low-frequency area and the rest is selected as a high-frequency area. Then a bit distribution calculation circuit 16 calculates the number of bits to be assigned to each block based on the mean value of the DCT coefficients thus found at every block. The quantization and coding of the high-frequency component are performed in such a way that the high-frequency component is quantized at a 2<m> level and one bit is used for code the DCT coefficients, with the (m-1) bits being assigned to the DCT coefficients. The information of the low- and high-frequency components thus found is stored in a recording and reproducing device 19.

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial Application Field> The present invention relates to an image data encoding method for the purpose of data compression, and particularly to an image data orthogonal transform encoding method using orthogonal transform.

<Prior Art> A two-dimensional full-color still image has a huge amount of information, but this image data has a large amount of redundancy, and by suppressing this redundancy, it is possible to compress the image data.

Conventionally, one such image compression method uses Fourier transform to remove redundancy of image data.

There is a method of converting each pixel value into a spatial frequency component using orthogonal transforms such as Hadamard transform, Barr transform, cosine transform, and Karhunen-Loeve transform (KL transform). In this type of image compression method, energy is concentrated in specific spatial frequency components due to the above-mentioned transformation, so a large number of bits are allocated to spatial frequency components with high energy, and a small number of bits are allocated to spatial frequency components with low energy. By allocating , the amount of data can be compressed. FIG. 9 shows a block diagram of an image data orthogonal transform encoding device implementing the above-described method.

This image data orthogonal transform encoding device operates as follows. That is, full-color still image data (for example, three primary colors of red (R), green (G), and blue (B),
The image size is NXN) is converted to NTSC by the preprocessing circuit l.
The system converts into a luminance signal (Y) and a color difference signal (1, Q). The reason for converting to the YIQ system in this way is as follows. In other words, the RGB system has a high degree of redundancy due to the high correlation between color components, but the YIQ system uses an orthogonal system with a low correlation between luminance and color difference, so redundancy is significantly reduced. In this system, the I and Q components are less noticeable compared to the Y component due to human visual characteristics, so it is possible to coarsely encode the I and Q components to reduce the overall code amount. be. Conversion from the RGB system to the YIQ system is performed using the following equation.

Next, the data converted into the YIQ system is converted into spatial frequency components having mutually independent conversion axes in an orthogonal conversion circuit 2. Then, the obtained orthogonal transformed image data is input to the encoding circuit 3, which allocates bits according to the energy of each frequency component, and performs quantization and encoding according to the number of allocated bits. and record/playback device 4.
to be recorded.

To reproduce this data and display it on the image reproduction device 7,
The data recorded in the recording/reproducing device 4 is read out and input to the decoding circuit 5, and the orthogonal transform image data decoded by the decoding circuit 5 is processed by the orthogonal transform circuit 2 by the inverse transform circuit 6. After performing the inverse transformation of the above conversion, the conversion from the YIQ system to the RGB system is performed to obtain an RGB signal. Then, a reproduced image is displayed on the image reproduction device 7 based on this RGB signal. The conversion from the YIQ system to the RGB system described above is performed by the following equation.

<Problem to be solved by the invention> Human visual characteristics are sensitive to low frequency components (flat areas with little change in pixel values in the spatial coordinate system), but sensitive to high frequency components (flat areas with little change in pixel values). It is known that even if there is some error in areas (parts with sharp edges), it is not noticeable.

However, in the conventional image data orthogonal transform encoding method described above, bit allocation is performed only based on the energy level of each spatial frequency component in the spatial frequency. Since a large number of bits are allocated, a large number of bits are also allocated to high frequency components to which human vision is insensitive, resulting in the problem that efficient compression of image data is not possible.

Therefore, the purpose of this invention is to divide the spatial frequency domain into a low frequency domain and a high frequency domain, and to allocate a smaller number of bits to the high frequency components even though the energy size is the same. An object of the present invention is to provide an image data orthogonal transform encoding method that can further increase the compression rate than conventional image data orthogonal transform encoding methods while suppressing deterioration of image quality.

Means for Solving the Problems> In order to achieve the above object, the present invention provides an image data orthogonal transformation in which two-dimensional full-color still image data is orthogonally transformed, and spatial coordinates are converted into spatial frequencies and encoded. In the encoding method, a plurality of region patterns are provided that divide the spatial frequency region into a low frequency region and a high frequency region, each using a predetermined threshold value, and the absolute value of orthogonal transformation data corresponding to the low frequency region of each region pattern is determined. The sum is calculated, and the low frequency region of the region pattern in which the sum of absolute values is the maximum is identified as the low frequency region of the orthogonal transformation data, and
The high frequency region of the above area pattern is identified as the high frequency region of the above orthogonal transform data, the low frequency region of the above orthogonal transform data is divided into a plurality of blocks, and the average value for each of the above blocks of the above orthogonal transform data is calculated. , allocate a large number of bits to the block with a large average value, while allocating a small number of bits to the block with a small average value, encode the low frequency domain in the orthogonal transformed data,
The method is characterized in that the high frequency region of the orthogonal transformed data is decomposed into bit brains, and run length encoding is performed for each bit plate.

<Example> Hereinafter, the present invention will be explained in detail with reference to illustrated embodiments.

FIG. 1 is a block diagram showing an embodiment of an image data orthogonal transform encoding device according to the present invention. This image data orthogonal transform encoding device includes a preprocessing circuit 11, an orthogonal transform circuit!
20 Frequency domain selection circuit 13° Quantization circuit for high frequency components 14. Encoding circuit for high frequency components 15. Bit allocation calculation circuit! 6. Quantization circuit for low frequency components 17. Encoding circuit 18 for low frequency components. Recording and reproducing device 19. Decoding circuit 20, inverse conversion circuit 21, and image reproduction device 22
It is roughly structured by.

The image data orthogonal transform encoding device having the above configuration operates as follows.

First, the encoding process will be explained.

The preprocessing circuit 11 processes full-color still image data (each pixel is red (R), green (G), and blue (B).
) data of image size NXN expressed by three signals),
It is used to convert Y (luminance signal) to l (color difference signal).

The image data subjected to YIQ transformation by the preprocessing circuit 111 is orthogonally transformed by the orthogonal transformation circuit 12. This orthogonal transformation includes many transformations such as Fourier transformation, Hadamard transformation, Barr transformation, and Karhunen-Loeve transformation (KL transformation).

In this embodiment, the following explanation will be given using discrete cosine transform (hereinafter referred to as DCT) as an example. Therefore, in the orthogonal transform circuit I2, the DCT is performed and the signals are converted into spatial frequencies having mutually independent conversion axes.

FIG. 2 shows an outline of the conversion by the above-mentioned DCT.

In FIG. 2, (x, y) is the position of a pixel on the screen, and Wxy is the pixel value at the position (X, F) on the screen. Moreover, (u, v) is a frequency component after conversion into a spatial frequency, and Wuv is a value of the DCT coefficient of the (u, v) component in the spatial frequency domain. The above conversion formula from Wxy to Wuv is expressed by the following formula.

(U, V=O, 1,..., t1) After converting the spatial coordinate system into a spatial frequency system by DCT according to the above equation, the frequency domain selection circuit 13 converts the low frequency Separate into regions and high frequency regions.

That is, the energy distribution of spatial frequency components in the spatial frequency domain differs depending on the image data. Therefore, in order to perform adaptive processing even for image data with different energy distributions of spatial frequency components, as shown in Fig. 3 (
Set four types of area patterns for determining the low frequency area of image data converted into a spatial frequency system (orthogonal transformed image data), as illustrated by two-dimensional arrays in a) to FIG. 3(d). . This area pattern is divided into a low frequency area (shaded area) and a high frequency area by a predetermined threshold value of the spatial frequency component. The area pattern in this embodiment is set so that the low frequency area is 1/4 of the total area.

Based on the energy distribution of the input orthogonal transformed image data, the optimal region pattern is selected from the above four types of region patterns as follows. First, the hatched portion (low frequency region) of each area pattern in FIGS. 3(a) to 3(d)
The sum of the absolute values of the DCT coefficients of the orthogonal transformed image data corresponding to is calculated for each image data. Next, from among the above four types of area patterns, the area pattern having the maximum value of the above-mentioned sum is selected. Then, the low frequency region in this selected region pattern is determined as the low frequency region of the orthogonal transformed image data. As the above-mentioned low frequency region, regions as shown in FIG. 3(e) to FIG. 3(1) 1 may be set.

Furthermore, the selection of the above-mentioned low frequency region will be explained in detail by giving an example. Figure 4 shows image data of size 16
FIG. 3 is a diagram expressing DCT coefficients in a two-dimensional array when orthogonally transformed into a x16 spatial frequency system. In this case, the sum of the absolute values of the DCT coefficients in FIG. 4 corresponding to the shaded areas (low frequency regions) in each region pattern in FIGS. 3(a) to 3(d) is as shown in FIG. 3(a). 1028 in the low frequency range of
21. In the low frequency region of Fig. 3(b), 102892,
In the low frequency region of FIG. 3(c), it is 103467, and in the low frequency region of FIG. 3(d), it is 103401. Therefore, the hatched portion of the area pattern in FIG. 3(c) with the largest sum value is selected as the low frequency area of the orthogonal image data in FIG. 4.

On the other hand, as the high frequency region, a portion (other than the shaded portion) that is not selected as the low frequency region in the region pattern of FIG. 3(c) is selected.

Next, the bit allocation calculation circuit 16. A method of quantizing and encoding low frequency component information performed by the quantization circuit 17 and the encoding circuit 18 will be described.

As shown in FIG. 5, the low frequency region of the orthogonal transformed image data of FIG.
DCT coefficients are divided into blocks each having one unit. Here, it is known that data after image data is orthogonally transformed by DCT follows a Gaussian distribution or a Laplace distribution.

In this way, since the orthogonally transformed image data after DCT follows the Gaussian distribution or the Laplace distribution, it is appropriate to take the logarithmic value of the absolute value of each DCT coefficient of this orthogonally transformed image data and perform logarithmic compression. Then, this logarithmically compressed DC
The average of the T coefficients is calculated and quantized for each block. The average value of the DCT coefficients in each block after logarithmic compression is calculated using the following equation.

Δ Here, σpq is the average value of the DCT coefficients of the block located at (p, q) in the spatial frequency domain, and Wij is the DCT coefficient value in the block. The above quantization is uniform quantization.

Based on the average value of the DCT coefficients obtained for each block in this manner, the bit allocation calculation circuit 16 calculates the number of bits to be allocated to each block as follows.

That is, assuming that the DCT coefficients follow a Gaussian distribution, the bit number distribution that minimizes the mean square error is expressed by the following formula.

Here, Rpq is the number of bits allocated to the block located at (p, q) in the spatial frequency domain, R is the average number of bits allocated to each block in the low frequency domain, and RI2 is 108
This is the average value of t l W ij l over the entire low frequency region.

In this way, when the total number of bits to be allocated to each block is determined, the DCT coefficient Wij is determined by the quantization circuit 17.
Then, quantization is performed by uniform quantization of the code of Wij and logs l Wij l - sum pq. Therefore, as information on the low frequency component that is sensitive to human vision, the Wij code obtained by encoding the quantized value in the encoding circuit 18, logs
Three types of information, 4pq and 9pq, are output.

Next, a method for quantizing and encoding information on high frequency components performed by the quantization circuit 14 and encoding circuit 15 for high frequency components will be described. The above-mentioned quantization and encoding of the high frequency components involves quantizing the high frequency components at the 2m level and assigning 1 bit to the sign of the DCT coefficient.
This is done by assigning (m-1) bits to the DCT coefficients.

- As an example, a case will be described in which m=3 and DCT coefficients arranged two-dimensionally as shown in FIG. 4 are used as orthogonal transformed image data. That is, the quantization circuit 14 performs quantization in the following manner, for example, depending on the range of the absolute value 1Wijl of the DCT coefficient in the high frequency region shown in FIG.

That is, when 0≦1Wijl<23, 023≦
When 1Wijl<45 1 When 45≦1Wijl<91 2 When 91≦1Wijl Quantize with 2 bits as 3. The results are shown in FIG. 6(a). 6th
Figure (b) shows the second bit plane when Figure 6 (a) is decomposed into bit planes, and Figure 61W (c) shows the first bit plane when Figure 6 (a) is decomposed into bit planes. Shows plain. Then, the encoding circuit 15 performs run-length encoding for each bit plane. Further, as shown in FIG. 6(d), the sign of the DCT coefficient Wij is determined by the encoding circuit 15 by setting 11- to 0 only for the coefficients whose quantization values shown in FIG. 6(a) are other than 0. encode. Therefore, the run length for each bit plane and the sign of the DCT coefficient are output as high frequency component information.

In this way, low frequency component information is encoded in block units with the number of bits corresponding to the average value of orthogonally transformed image data, and high frequency component information is run length encoded for each bit plane. The low frequency region to which human vision is sensitive is encoded with a number of bits depending on the amount of energy, and the high frequency region to which human vision is insensitive is encoded with a small number of bits. Therefore, the amount of image data can be further compressed while suppressing deterioration of image quality.

Further, information on high frequency components may be encoded as follows. That is, all coefficients that take values other than 0 in FIG. 6(a) are set to l as shown in FIG. 8(a). Then, extract only the DCT coefficient in the frequency component set as l, and calculate the DCT coefficient value including the sign as shown in Figure 8 (
Arrange them in order as shown in b). Next, Fig. 8(a)
The sequence of coefficients shown in FIG. 8(b) is encoded with 3 bits/coefficient and output to the recording/reproducing device I9. In addition, as a method of transmitting the coefficient string shown in FIG. 8(b) above, especially when there are many values above the threshold value, the same number of bits should not be given uniformly, but to coefficients with small absolute values that appear less frequently. The compression ratio may be prevented from decreasing by allocating a small number of bits to the coefficients and allocating a large number of bits to coefficients that appear frequently and have large absolute values.

The information on the low frequency components and high frequency components obtained as described above is stored in the recording/reproducing device 19 (magnetic tape, floppy disk, etc.).

Next, the decoding process will be explained.

The data read from the recording/reproducing device 19 is input to a decoding circuit 20, where the decoding circuit 20 performs decoding processing as described below.

In the case of information on low frequency components, the number of bits allocated to each block is calculated using the same method as in the case of encoding above from the DCT coefficient coefficient mean value σpq for each block read from the recording/reproducing device 19. do. Then, determine the quantization width from the allocated number of bits and
By calculating the value of I−pq and subtracting the value of Δσpq, log
Find l. Furthermore, decoding is performed by calculating the power of 2 of logs l Wij I to obtain the value of 1WiN, that is, the DCT coefficient value.

In addition, in the case of information on high frequency components, the run length code read from the recording/reproducing device 19 is used as shown in FIG.
Reconstruct the bit-branes in a) and FIG. 6(b). The quantized value shown in FIG. 6(a) is obtained from the above bit brain, and the obtained quantized value is combined with the DCT shown in FIG. 6(d).
Decoding is performed by finding DCT coefficients from code data of the coefficients. FIG. 7 shows the result of decoding the quantized values in FIG. 6(a). Here, reference numeral 1 in FIG. 6(a)
, 2.3 are decoded as 32, 64, and 128, respectively.

After both low frequency components and high frequency components are decoded and DCT coefficients are calculated as described above, these two types of D
A spatial frequency system is reconstructed by combining the CT coefficients, and this data is input to the inverse transform circuit 21. The inverse transformation circuit 21 inversely transforms the spatial frequency system into a spatial coordinate system. Here, D
CT coefficient is Wuv.

When the pixel value is WXy and the image size is NXN, the inverse discrete cosine transform is defined by the following equation.

(x, y: o, 1..., tr) The pixel value calculated by the above formula is a luminance signal (Y) and a color difference signal (1, Q), so it is converted into red (R), The data is converted into three signals of green (G) and blue (B) and input to the image reproducing device 22, and the image reproducing device 22 obtains a restored image based on the RGB signals.

Note that parameters such as the quantization width, threshold value, and block size used in the image data orthogonal transform encoding method of the present invention can be determined experimentally or empirically.

<Effects of the Invention> As is clear from the above, the image data orthogonal transform encoding method of the present invention provides a plurality of area patterns that divide the spatial frequency area into a low frequency area and a high frequency area, and The sum of absolute values of the orthogonal transform data corresponding to the low frequency region of The high frequency region is identified as the high frequency region of the orthogonal transform data, the average value of the orthogonal transform data for each block of the low frequency region of the orthogonal transform data is calculated, and based on the magnitude of this average value, the The number of bits is calculated to encode the low frequency domain, the high frequency domain of the orthogonal transform data is decomposed into bit planes, and run length encoding is performed for each bit plate. , it is possible to reduce the number of bits for high-frequency regions to which human vision is insensitive, and increase the number of bits for low-frequency regions to which human vision is sensitive, resulting in higher compression rates than conventional methods while suppressing image quality deterioration. Image data can be compressed with .

[Brief Description of the Drawings] Fig. 1 is a block diagram of an embodiment of an image data orthogonal transform encoding device according to the present invention, and Fig. 2 is a DCT of image data.
Figure 3 is an explanatory diagram of each area pattern, Figure 4 is the DC after orthogonal transformation by DCT.
FIG. 5 is a diagram showing an example of a block in a low frequency domain; FIG. 6 is a diagram showing an example of code data of quantized values, bit branes, and DCT coefficients in a high frequency domain; Figure 7 shows the results of decoding the quantized values in the high frequency domain shown in Figure 6, Figure 8 shows another example of encoding in the high frequency domain, and Figure 9 shows the conventional image. FIG. 2 is a block diagram of a data orthogonal transform encoding device. 11... Moe processing circuit, 12... Orthogonal transformation circuit,
13... Frequency domain selection circuit, 14.17... Quantization circuit, 15.18... Encoding circuit, 16... Bit allocation calculation circuit, 19... Recording/reproducing device, 20... Decoding circuit, 21...
- Inverse conversion circuit, 22... image reproduction device.

Claims (1)

[Claims] (1) In the image data orthogonal transformation encoding method, which performs orthogonal transformation on two-dimensional full-color still image data and converts the spatial coordinates into spatial frequencies and encodes them, the spatial frequency domain is converted to a low frequency by a predetermined threshold value. A plurality of area patterns are provided that are divided into areas and high frequency areas, and the absolute value sum of the orthogonal transformation data corresponding to the low frequency area of each of the area patterns is calculated, and the low frequency area of the area pattern where the above absolute value sum is the maximum is calculated. Identifying a frequency domain as a low frequency domain of the orthogonal transformation data, identifying a high frequency domain of the area pattern as a high frequency domain of the orthogonal transformation data, and dividing the low frequency domain of the orthogonal transformation data into a plurality of blocks. Then, calculate the average value for each block of the orthogonal transformed data, and allocate a large number of bits to blocks with a large average value, while allocating a small number of bits to blocks with a small average value, and then Image data orthogonal transform encoding characterized by encoding a low frequency region in orthogonal transform data, decomposing the high frequency region of the orthogonal transform data into bit planes, and performing run length encoding for each bit plate. method.