JPH03224363A - Compressed data quantity control system - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed Description of the Invention] [Industrial Application Field] This invention improves the compression ratio when compressing still image data and transmitting or recording it so that the amount of data after compression is less than or equal to the required amount of data. This invention relates to a method for controlling the amount of data to be controlled.

[Conventional technology]

In order to standardize the natural image coding method, "'Ba5e-
1ine System'' or 'Extended Sy
Various international standardization methods such as "stem" have been proposed.

Figure 4 shows “Ba5elineS”, one of the international standardization methods.
This system divides one input image into multiple blocks of 8×8 pixels, and performs two-dimensional discrete cosine transformation (D CT: Discrete Cosi
neTransform) (processing PL), and quantization is performed by dividing the obtained OCT coefficients by each threshold of a quantization matrix consisting of 8 x 8 thresholds (processing PL).
2). FIGS. 5 and 6 are examples of quantization matrices for luminance signals and color difference signals.

The difference between the direct current (DC) component of the quantized DCT coefficient and the DC component quantized in the previous block is taken, and the number of bits of the difference is Huffman encoded.

The alternating current (AC) component is converted into a one-dimensional sequence by performing zigzag scanning within the block, and then subjected to two-dimensional Huffman encoding using the number of bits of effective coefficients and the number of consecutive zeros (invalid coefficients) (Process P3 and P4).

FIG. 7 shows an example of a zigzag scan table.

Note that during quantization in process P2, each threshold value of the quantization matrix is multiplied by a certain coefficient (scale factor), and then the DCT coefficient is divided. The scale factor is a value expressed as 2SJ (S=0.±1゜±2....), and each threshold value of the quantization matrix is multiplied by the scale factor 2S.The digital data of each threshold value is bit-shifted. The image quality and compression rate of the compressed image are adjusted by this scale factor.

The data compressed in this way is decompressed by a process opposite to process PL-P4. That is, Huffman decoding in process P5, decoding of DC and AC components in process P6, inverse quantization in process P7, and inverse DCT (IDCT) in process P8.

By the way, since this system performs data compression using a Huffman code, which is a variable length code, the total amount of data after compression cannot be known until the compression process (processes P1 to P4) is completed. Therefore, if it is necessary to encode within a preset data amount range, some kind of data amount control is required. Conventionally, compression was performed using multiple types of scale factors, the amount of data after compression was measured in each case, the relationship between the scale factor and the amount of data after compression was found, and the scale factor corresponding to the amount of data after compression was calculated. By analogy, data compression is performed using this analogous scale factor.

It has been recognized from the measurement results of many images that the relationship between the amount of data after compression and the scale factor is as follows.

[Data amount] = Alog [scale factor] + B...
...■ (A, , B: constants determined by measurement points) Therefore, it is possible to obtain A and B from the measurement results and estimate the scale factor for the required amount of data after compression. In the experimental results of 8 images, ±5% of the required data amount
It has been reported that it can be controlled with the following error (198
Proceedings of the 9th IEICE Autumn National Conference D-45).

(Problem to be Solved by the Invention) By the way, according to the data amount control method described above, when recording compressed data on a recording medium, the image data for one sheet that has already been recorded is erased and the data is used in that area. When recording, there is a problem that if there is a positive error in the estimated scale factor, the compressed data to be recorded will not fit into that area.

In addition, in the above processing procedure, the scale factor is treated as a real number, and if the scale factor is used in the form of 2s, the quantization step width will be controlled with a change width of 2 times or 172 times, so the error in the amount of data will be is expected to grow even larger.

An object of the present invention is to provide a compressed data amount control method that ensures that the amount of data after compression is less than or equal to the required amount of data.

[Means to solve the problem]

In this invention, - digital images are divided into l blocks n×
Divide into a plurality of blocks each consisting of n pixels, perform discrete cosine transformation for each block, and convert the n×n transform coefficients obtained by the transform into predetermined coefficients 2s (S=0.±).
1. ±2. ...) is multiplied by each threshold value of a quantization matrix consisting of n×n threshold values to perform quantization,
An image data compression method in which quantized data is variable-length coded, and the width S of the coefficient 2s has first and second values S1.
and S2 respectively, perform quantization and encoding, and estimate the value S3 of the width S at which the data amount after compression becomes the desired setting value ■3 from the resulting compressed data amounts ■1 and ■2. , perform quantization and encoding by setting the width S to this estimated value Sa, and if the amount of data after compression exceeds the set value V3, add "1" to the width S to increase the quantization step width. , performs quantization and encoding again, and repeats these processes until the amount of data after compression becomes less than or equal to the set value V3, so that the amount of data after compression becomes less than or equal to the set value V8.

[Operation] This invention quantizes the transform coefficients obtained by discrete cosine transform by dividing each threshold value of a quantization matrix, and performs variable length encoding such as Huffman encoding on the quantized transform coefficients. When compressing data, each threshold value of the quantization matrix is multiplied by a predetermined coefficient 2S, and the quantization step width is changed by changing the value of width S, so that the amount of data after compression matches the desired setting value. First, a specific value S1 is set as the width S of the coefficient 2s, the above-mentioned quantization and encoding are performed, and the resulting compressed data amount is set to 1. Then, another specific value S2 is set to the coefficient 2s
The width of S is set as S, quantization and encoding are performed in the same way, and the amount of data after compression obtained is 2.

Since there is a specific relationship between the amount of data after compression and the scale factor, the value St of the width S of Kale Fakku 2S, S2
and data amount v1. Estimate the value S3 of the width S for the desired data amount ■8 from v2, perform quantization and encoding using this estimated value S8, and process if the resulting compressed data amount is the required data amount V3. end.

If the data amount after compression exceeds the data amount ■3, "l" is added to the width S to increase the quantization step width, and data compression is performed again. If the amount of data still exceeds the required data amount Va, "1" is further added to the width S to compress the data. These series of processes are repeated until the amount of data after compression becomes less than or equal to the required amount of data (2)3, and the processing is terminated when the amount of data after compression becomes less than or equal to the required amount of data, V8.

In this way, the amount of data after compression will definitely be less than the desired amount of data, and even if you erase one image's worth of image data that has already been recorded and record new compressed data in that area, you will be able to There is no problem that the compressed data cannot fit into the area.

(Embodiment) FIG. 1 is a schematic diagram showing an embodiment of the processing procedure of the compressed data amount control system according to the present invention, and the same parts as in FIG. 4 are given the same reference numerals and will be described.

First, the input image data is divided into n×n pixels in the horizontal and vertical directions.
Pixels are divided into multiple blocks of, for example, 8×8 pixels, and each block is subjected to two-dimensional discrete cosine transformation (DCT).
) is applied (processing Pi).

DCT is a type of orthogonal transformation in the frequency domain. Let F(u, v) be the transform coefficient, and f (i, j) be the input image data for one block. However, C(w)=i/J
2 (w=o)-1 (W≠0), and the obtained transformation coefficient F (u, v) indicates a component obtained by decomposing one block of input image data into spatial frequencies.

The conversion coefficient F (0, 0) is the input image data f (i, j)
It shows a value (DC component) proportional to the average value of n×n pixels of F(u,v), and as u and v become larger, a component (AC component) of higher spatial frequency shows.

For the two-dimensional DCT coefficients obtained in this way, n×
Quantization is performed by dividing a value obtained by multiplying each threshold value of a quantization matrix consisting of n threshold values by a scale factor 2s (processing P2). The multiplication process for each threshold value of the quantization matrix by the scale factor 2s corresponds to bit-shifting each threshold value of the quantization matrix as described above, and the increase or decrease in the amount of data after compression can be adjusted by this scale factor.

Next, for the quantized transform coefficient F'(u, ν), DC
For the component, a difference is taken from the DC component quantized in the previous block (processing P3), and the number of bits of the difference is Huffman encoded (processing P4). For the AC component, zigzag scanning is performed in the order shown in Figure 7 to convert it into a one-dimensional number sequence, and then run-length encoding is performed to compress the number of consecutive zero data (processing P3), and further run-length encoding is performed. Number of consecutive zero data and effective coefficient Pip1
- Perform two-dimensional Huffman encoding with loss data (processing P
4).

Huffman encoding does not use the quantized coefficient values themselves for both DC and AC components, but performs Huffman encoding on the number of bits necessary to express the values. In addition to the Huffman code, the value of the number of bits is added as additional information. For example, if the quantized coefficient is 2 (decimal number), it will be expressed in binary as "000...010"°, but the number of bits required to express this, 2, will represent this value. Huffman encoding is performed as a value, and 2-bit data "10°" is added as an additional bit.

On the other hand, if the quantized coefficient is negative, data obtained by subtracting 1 from the additional bit is added. For example, if the quantized coefficient is −
2 (decimal number), when expressed in binary number (2's complement representation), it becomes "111...110°", and the lower 2
The bit becomes an additional bit, and '01°°, which is "10" minus "1", is added as an additional bit. By doing so, when the quantized coefficient is positive, the additional bit starts with 1, and when it is negative, it starts with O, making it easy to determine whether it is positive or negative.

Next, the amount of data after compression is measured, and if the total amount of data exceeds the required amount of data, the width S of the scale factor 2S is adjusted to increase the quantization step width in processing P2, and the amount of data after compression is (Processing P
IO).

The operation will be explained with reference to the flowchart in Figure 2.
First, set the value of the width S of the scale factor 2S to a specific value S1 (step T1), perform data compression processing in processes P2 to P4 (step T2), and set the obtained data amount after compression to 1 (step T3).

Next, set the value of the width S of the scale factor 2S to another specific value S2 (St≠32).
Data compression processing in processes P2 to P4 is performed (step T5), and the resulting compressed data amount is set to 2 (step T6).

Since the amount of data after compression and the scale factor are in the relationship shown in the above equation (2), the values S i q V i and the value S2. V2 is substituted to obtain the values A and B, and the value of the width S of the scale factor 2s corresponding to the required data amount (3) is found from the equation (2) and set as the estimated value Sa (step T7).

The values S1 to Sa and the data 11tVt to ■3 have a relationship as shown in FIG.

Next, the estimated value S3 is scaled by 1113 with a scale factor of 2S.
Then, in step T8), data compression processing in processes P2 to P4 is performed (step T9), and it is determined whether the resulting compressed data amount is less than or equal to the required data amount V3.Step T10), the data amount ■ If the number is 3 or less, the process ends.

If the amount of data after compression exceeds 3, the width S
Add "1" to (Ss) (step T11), perform the data compression process in processes P2 to P4 again (step T9), and judge again whether the compressed data amount is less than the predetermined data amount ■3 (Stephenbu T10). In this way, the processing of steps T9 to TIO is repeated, and when the amount of data after compression becomes less than the required data amount (3), the processing is terminated.

[Effects of the Invention] According to the present invention, data is compressed using two scale factors, the amount of data after compression is measured, and a scale factor corresponding to the required amount of data after compression is estimated from the measurement result, Data is compressed based on this estimated value, and the compression process is repeated by sequentially increasing the estimated scale factor value until the amount of data after compression becomes less than or equal to the requested amount of data. Since data compression is terminated when the amount is less than the required amount, it is possible to ensure that the amount of data after compression is less than the required amount of data.

[Brief explanation of drawings]

Fig. 1 is a diagram showing the processing procedure of the compressed data amount control method according to the present invention, Fig. 2 is a flowchart for explaining the operation of Fig. 1, and Fig. 3 is the relationship between the scale factor and the amount of data after compression. 4 is a diagram showing the processing procedure of conventional compression/expansion processing. FIG. 5 is a table showing the quantization matrix of the luminance signal. FIG. 6 is a table showing the quantization matrix of the color difference signal. FIG. 7 is a table showing a zigzag scan table. Flowchart Figure 2

Claims (1)

[Claims] One digital image is divided into a plurality of blocks each consisting of n×n pixels, and discrete cosine transform is performed for each block, resulting in n×n transform coefficients. , respectively with a predetermined coefficient 2^S (S=0, ±1, ±2, ・
An image data compression method that performs quantization by dividing by each threshold of a quantization matrix consisting of n×n thresholds multiplied by . The width S of the coefficient 2^S is set to the first and second values S_1 and S_2, and the above quantization and encoding are performed, and the desired amount of data after compression is determined from the resulting amounts of compressed data V_1 and V_2. If the value S_3 of the width S is estimated to be the set value V_3, the width S is set to this estimated value S_3, the quantization and encoding are performed, and the amount of data after compression exceeds the set value V_3. is "1" in the width S above.
'' to increase the quantization step width, perform the above quantization and encoding again, and repeat these processes until the amount of data after compression becomes less than or equal to the set value V_3, and the amount of data after compression becomes equal to or less than the above setting value V_3. A compressed data amount control method characterized by controlling the amount of compressed data to a set value V_3 or less.