JPH09284767A - Picture compression system and picture compression method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accelerate a compression processing by parallelly performing the process of preparing code data by the two kinds of compression degrees after generating the DCT coefficient of a block 1 and the process of generating the DCT coefficient of the block 2. SOLUTION: A DCT part 2 performs a DCT processing to the picture data of the block 1 first, and when a controller 8 detects the end, a similar processing is performed to the blocks 2-(n). Then, for the DCT coefficient generated by ending the DCT processing to the block 1, the controller 8 instructs a quantization part 4 to perform quantization in the quantization table 11 of a scale factor SFx and quantized data are generated. When the controller 8 detects generation end, an encoding processing is instructed to an encoding part 5 and the code data of the block 1 are generated. Similarly thereafter, the processing is continued until the last block (n) and the picture data of one frame are compressed.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a digital image processing system, and more particularly to an image compression system and an image compression method capable of compressing a digital image to reduce the amount of data.

[0002]

2. Description of the Related Art One of those that use an image compression system is a digital still camera. A digital still camera shoots a digital still image by pointing a lens at a subject and pressing a shutter button. The image formed via the lens is converted into an electric signal, compressed, and stored in a replaceable memory card or the like. Data compression is a process for reducing the amount of data and storing a large amount of image data in a memory card.

The amount of coded data obtained by data compression of a digital image differs depending on the spatial frequency distribution of the digital image and the like. For example, for a digital image containing many high-frequency components, the amount of code data cannot be reduced much. On the other hand, with respect to a digital image having few high frequency components, the amount of code data can be considerably reduced. That is, the amount of code data generated by data compression generally differs depending on the type of digital image, although it differs depending on the data compression method.

[0004] Code data obtained by data compression is stored in a storage medium such as a memory card. The memory card has a storage capacity of, for example, 1 Mbyte, and in that case, data of 1 Mbyte or more cannot be stored.

It is necessary to inform the photographer of the remaining number of recordable sheets in order to prevent the code data from being written in the memory card exceeding 1 Mbyte or for the convenience of the photographer. If the code data to be data-compressed has the same data amount for each image regardless of the type of digital image, the number of digital images that can be recorded on the memory card can be easily notified to the photographer.

However, when the amount of code data is variable, it is impossible to notify the photographer of the remaining number. If the code data amount of the image to be captured is small, a large number of images can be recorded, and if the code data amount of the image to be captured is large, only a small number can be recorded.

Therefore, when compressing the data of the digital image, it is necessary to perform the fixed length processing of the code data. By performing the fixed length processing, any kind of digital image can be converted into a substantially constant amount of code data. The fixed length process is a process for compressing one digital image (one frame) to generate fixed-length code data. If the code data has a fixed length, the photographer can be notified of the remaining number.

Next, the fixed length processing will be described. To perform the fixed length processing, first, statistical processing is performed as preprocessing, and the compression degree of data compression is adjusted according to the result of the statistical processing to generate fixed length code data.

When the photographer presses the shutter button, a digital image is captured. Next, statistical processing is performed on the captured digital image. The statistical process is a process of estimating how much code data is generated when the captured digital image is compressed.

When the statistical processing is completed, compression processing and storage processing are performed. As a result of the statistical processing, if it is estimated that a large amount of code data is likely to be generated, the compression degree may be set higher and compression may be performed. If it is estimated that the code data is likely to be generated in a small amount, the compression degree may be set to be low and the compression may be performed. Code data generated by data compression always has a substantially constant data amount.

[0011] After that, the code data that has been subjected to the data compression by the storage process is recorded on the memory card. Above,
A series of processes from capture of the digital image to recording on the memory card ends.

[0012]

In the image compression processing, there is the following method for performing the fixed length processing. First, as the statistical processing, the compression processing is performed once with the standard compression degree. As a result, when a data amount larger than the target data amount is generated, the compression degree is set higher than the reference compression degree. On the other hand, when code data of a smaller amount than the target data amount is generated, the compression degree is set lower than the reference compression degree. Formal compression processing is performed with the set compression degree to generate coded data of an image.

However, since the relationship between the compression degree and the code data amount is not constant, the error between the target data amount and the generated code data amount varies depending on the type of image. The accuracy of the fixed length is low only by performing the statistical processing once.

In order to make the fixed length more accurate, there is a method of performing the statistical processing twice or more to determine the compression degree. The case where the statistical processing is performed twice will be described as an example. First, as the first statistical processing, compression processing is performed at the first standard compression degree,
Estimate the amount of code data. Next, as the second statistical processing, compression processing is performed with a second reference compression degree different from the first reference compression degree, and the code data amount is estimated. The formal compression degree is set in consideration of the above two code data amounts. For example, when the target data amount is between the two code data amounts, a formal compression degree may be set between the first reference compression degree and the second reference compression degree. Formal compression processing is performed with the set compression degree to generate coded data of an image.

However, in this method, the statistical processing is performed twice and the formal compression processing is performed once, so that the compression processing is performed three times in total, which requires a great deal of time.

An object of the present invention is to provide an image compression system which performs a high-speed and high-precision fixed length processing.

Another object of the present invention is to provide an image compression method for performing high-speed and high-precision fixed-length processing.

[0018]

An image compression system according to the present invention separates image data supply means for supplying image data by dividing it in block units, and image data in block units supplied from the image data supply means. D for generating DCT coefficients in block units by cosine transform (DCT)
The CT means and the DCT means are one block of DCT
Each time a coefficient is generated, code data generating means for continuously generating code data with the first and second compression degrees for the DCT coefficient, and first and second compression degrees generated by the code data generating means. The target of the amount of code data for each of the supplied blocks according to the counter for accumulating all the supplied blocks and the amount of code data for both the first and second compression degrees generated by the counter. And a compression degree estimating means for estimating a compression degree for generating coded data of a data amount.

In the image compression method of the present invention, the step of generating the DCT coefficient of the first block by performing the discrete cosine transform (DCT) on the image data of the first block, and the generation of the DCT coefficient of the first block. Is completed, DCT of the image data of the second block is performed to generate the DCT coefficient of the second block, and when the generation of the DCT coefficient of the first block is finished, the DCT coefficient of the second block is finished. Of the first block by the time the generation of
Generating code data at a first compression degree for the T coefficient and subsequently generating code data at a second compression degree.

When the DCT means finishes generating the DCT coefficient of the first block, the code data generating means determines that DCT coefficient.
For the coefficient, code data is generated with the first compression degree, and subsequently code data is generated with the second compression degree. After generating the DCT coefficients of the first block, the DCT means can generate the DCT coefficients of the second block. DCT
When the means have finished generating the DCT coefficients for the first block,
It is possible to parallelize the step of the code data generating means generating the code data with at least two kinds of compression degree and the step of the DCT means generating the DCT coefficient of the second block. By performing the steps in parallel, the speed of the compression processing can be increased. Further, by generating the coded data with at least two types of compression degree, it is possible to perform a highly accurate fixed length processing.

[0021]

FIG. 2 is a flow chart showing the processing procedure performed by the image compression system according to the embodiment of the present invention.

In step S1, statistical processing is performed. In this statistical process, the coded data amount for two types of compression is calculated during one compression time. Normally, at least two compression processing times are required to calculate the coded data amounts for two types of compression degrees. The feature of the statistical processing here is that code data for two types of compression is efficiently generated and processed in a short time. After the code data is generated, the formal compression degree is determined based on the amounts of the two types of code data.

In step S2, a formal compression process is performed with the compression degree determined by the statistical process. By this compression process,
Code data of the image is generated. The amount of code data to be generated can approach the target amount of data with high accuracy. Thus, the fixed length compression process is completed.

FIG. 1 is a block diagram showing the arrangement of an image compression system according to this embodiment. This image compression system uses JPE, which is a standard compression method for digital still images.
Code data of G (joint photographic expert group) system is generated. The resources of the conventional JPEG system can be used as they are.

The image compression system includes an image memory 1, a discrete cosine transform (hereinafter referred to as DCT) unit 2, a DCT coefficient memory 3, a quantization unit 4, an encoding unit 5, an encoded data amount counting unit 6, and a scale factor determining unit. 7 and a controller 8. The controller 8 exchanges timing signals with all other processing blocks and adjusts timing between the processing blocks.

Next, each processing block will be described.
The image memory 1 is, for example, a DRAM or a flash memory and stores one frame of image data. Image data is normally stored in the image memory 1 in a raster format. The image data is composed of a plurality of pixel data.

The raster format is an array of the following pixel data for one frame image. First, the images are arranged sequentially starting from the pixel at the upper left corner of the image toward the right horizontal direction. After reaching the rightmost pixel, subsequently, starting from the leftmost pixel of the next line, the pixels are sequentially arranged in the right horizontal direction. Hereinafter, the process is similarly performed up to the bottom line. The pixel at the lower right corner is the last data.

Since the image compression system basically performs processing for each block of 8 × 8 pixels, the image memory 1 converts the image data from the raster format to the block format and the DC format is used.
Supply to the T section 2. A monochrome image has one type of image data. A color image is divided into luminance data and color data, but raster / block conversion is performed by using each as separate image data.

The block format is an array of the following pixel data for one frame image. One frame image is divided into a plurality of blocks. One block is 8 × 8 pixels. The order of the blocks in one frame starts from the block at the upper left corner and is arranged in the right horizontal direction as in the above-mentioned raster format. The last block is the block in the lower right corner. The arrangement of the pixel data in the block is also similar to the raster format, starting from the pixel data at the upper left corner in the block and arranged in the right horizontal direction. The last pixel data is the pixel data at the lower right corner in the block.

The DCT unit 2 is supplied with block-type image data I. In the following, processing is performed with one block of image data as one unit. That is, in JPEG compression, one image is divided into blocks of 8 × 8 pixels, and the following processing is performed for each block.

The DCT section 2 performs DCT processing on the image data I in block units. In the DCT processing, the image data I is interposed between a transposed cosine coefficient matrix ^Dt and a cosine coefficient matrix D, and a matrix operation is performed to obtain a DCT coefficient F.

F = D ^t ID Here, the DCT coefficient F is an 8 × 8 matrix and indicates a spatial frequency component.

The DCT coefficient memory 3 is, for example, a DRAM or an SRAM, and the DCT coefficient F generated by the DCT unit 2
Is stored.

Next, the structure of the quantizer 4 will be described. The memory 11 stores a reference quantization table Q.

FIG. 3 shows an example of the reference quantization table Q. As described above, the image compression system compresses data in 8 × 8 block units, and accordingly, the quantization table Q is composed of an 8 × 8 matrix.

The reference quantization table Q is a quantization table for performing data compression with a standard compression degree. The quantization process divides the 8 × 8 DCT coefficient F by the corresponding coefficient in the quantization table Q. The DCT coefficients have lower spatial frequency components in the upper left direction of the matrix, and higher frequency components in the lower right direction. The reference quantization table Q indicates that the lower frequency component is finer as a whole and the higher frequency component is coarsely quantized as a whole. Generally, data compression is performed by removing information on high-frequency components of image data in consideration of human visual characteristics and high-frequency components having a lot of noise.

In FIG. 1, the multiplier 12 multiplies the reference quantization table Q by the scale factor SF. That is,
All the elements of the matrix of the reference quantization table Q are multiplied by the scale factor SF. The multiplier 12 uses the quantization table S
Outputs F / Q. The quantization table SF · Q is stored in either the memory 13 or the memory 14.

A quantization table A is stored in the memory 13, and a quantization table B is stored in the memory 14.
The quantization table A and the quantization table B have different scale factors SF. The compression degree of the code data is determined by the value of the scale factor SF. That is, the difference between the quantization table A and the quantization table B indicates the difference in compression degree. The quantization table A is represented by Q · SFa, and the quantization table B is represented by Q · SFb.

Either the quantization table A or the quantization table B is supplied to the divider 15 as SF · Q. When performing the statistical processing, the quantization table A is used as the first reference table and the quantization table B is used as the second reference table.
Used as a reference table for.

The divider 15 converts the DCT coefficient Fuv stored in the DCT coefficient memory 3 into the quantization table SF ·
Divide by Quv and output the quantized coefficient Ruv. Rounding round means rounding to the nearest integer.

Ruv = round [Fuv / (SF · Quv)] The encoding unit 5 performs an encoding process on the quantized data Ruv. The encoding process includes processes of run-length encoding and Huffman encoding. The run-length encoding can perform high compression on data in which the value of 0 continues continuously. In the quantized data Ruv, many zeros tend to be collected in the lower right part (high-frequency component) of the matrix. By utilizing this property and performing run-length encoding of the matrix Ruv of quantized data by zigzag scanning, high compression can be performed. Zigzag scanning is a method of sequentially scanning from low frequency components to high frequency components.

The coding unit 5 performs Huffman coding after performing run-length coding, and generates coded data.

The code data amount counting section 6 is provided with a counter A.
And a counter B. The counter A counts the code data amount NA when the quantization is performed by the quantization table A and the code data is generated. The counter B counts the code data amount NB when the quantization is performed by the quantization table B and the code data is generated.

In other words, during the statistical processing, the counter A counts the code data amount NA when compressed with the first reference compression degree (quantization table A). The counter B counts the code data amount NB when compressed with the second reference compression degree (quantization table B).

FIG. 4 shows how the coded data amount counting unit 6 counts the coded data amount. One frame image is composed of, for example, n blocks. Code data is generated in block units. The counters A and B accumulate the amount of code data of all blocks (n blocks) and calculate the amount of code data of one frame image.

The counter A calculates the code data amount NA when the compression is performed using the quantization table A. The counter B calculates the code data amount NB when the compression is performed using the quantization table B.

Here, an example is shown in which the scale factor SFa for the quantization table A is larger than the scale factor SFb for the quantization table B.
That is, if the quantization table A is used, the quantization table B
You can achieve higher compression than with.

The code data amount NA when the same image data is compressed using the quantization table A is smaller than the code data amount NB when the quantization table B is used for compression.

In FIG. 1, the scale factor determining unit 7
Determines the formal scale factor SFx based on the two types of code data amounts NA and NB. The scale factor SFx is estimated as a compression degree for generating coded data of the target data amount NX. Scale factor SF
When x is obtained, the statistical processing (step S1 in FIG. 2) ends.

After the statistical processing is completed, compression processing (step S2 in FIG. 2) is performed. In the compression process, the scale factor SFx is input to the quantizer 4 as the scale factor SF. If compression is performed using the scale factor SFx, code data close to the target data amount NX can be generated.

FIG. 5 is a graph for explaining the processing of the scale factor determining section 7. The vertical axis is the scale factor, and the horizontal axis is the code data amount. The scale factor determination unit 7 holds the scale factor as a function of the amount of code data. As an example, a case where the scale factor is proportional to the code data amount will be described.

When the compression is performed using the scale factor SFa, the code data amount NA is obtained. When the compression is performed using the scale factor SFb, the code data amount NB is obtained. Based on these data, characteristics (approximate by a straight line) as shown in FIG. 5 are set.

The target data amount NX is designated from the outside. A scale factor corresponding to the target data amount is obtained from the above-described characteristics. It is understood that the compression may be performed using the scale factor SFx in order to obtain the target data amount NX.

The scale factor SFx can be obtained from FIG. 5 as follows. (NB-NA) :( NX-NA) = (SFa-SFb) :( SFa-SFx) (NB-NA) * (SFa-SFx) = (NX-NA) * (SFa-SFb) (SFa-SFx) ) = (NX-NA) * (SFa-SFb) / (NB-NA) SFx = SFa-{(NX-NA) * (SFa-SFb) / (NB-NA)}

Note that the scale factor S
In addition to obtaining Fx, the scale factor SFx may be obtained by the above approximate expression. In addition, in addition to the calculation formula,
Scale factor SFx using lookup table
May be determined. Further, the relationship between the scale factor and the code data amount is not limited to the proportional relationship, but may be set to another relationship such as an inverse proportionality. However, it is desirable that the scale factor SFx can be determined with a short and simple configuration.

Further, when compression processing is performed using the scale factor SFx to generate code data, the scale factor obtained above is further multiplied by a safety factor so that the code data amount does not exceed the target data amount. You may do it.

Next, in the image compression system of FIG.
Each processing procedure will be described separately for the statistical processing and the compression processing.

First, the statistical processing will be described. (1) Statistical processing In the statistical processing, two types of code data amounts NA and NB are calculated in one compression processing time. This is D in the compression process.
This is made possible by utilizing the property that the processing time in the CT unit 2 is overwhelmingly long.

The processing time in the DCT unit 2 is 5 in total.
It accounts for ~ 80%. The time required for DCT processing (DCT unit 2) and the time required for quantization processing (quantization unit 4) of one block (8 × 8 pixels) of image data will be compared.

The DCT unit 2 performs the following number of calculations. Here, a case where a normal DCT algorithm is used is shown.

Multiplication 1024 times Addition 896 times The quantizer 4 performs the following number of calculations.

Multiplication 64 (= 8 × 8) times As described above, the number of calculations performed by the DCT unit 2 is orders of magnitude greater than that of the quantization unit 4, and the processing time is overwhelmingly long.

FIG. 6 is a timing chart showing the timing at which the image compression system of FIG. 1 performs the statistical processing.
The horizontal axis indicates time.

The DCT section 2 carries out processing in the order of processing α1, processing α2 and processing α3. The processing α1 is performed in block 1 (1
This is a DCT process performed on the image data of the (th block). Process α2 is a DCT process performed on image data of block 2 (second block), and process α3 is performed on image data of block 3 (third block).

The DCT unit 2 starts the DCT processing α1 for the image data of the block 1 at time t0. When detecting the end of the DCT processing α1 at time t10, the controller 8 sends the block 2 to the DCT unit 2.
To start the DCT process α2 for the image data. Subsequently, when the end of the DCT process α2 is detected at time t20, the DCT unit 2 is instructed to start the DCT process α3 for the image data of the block 3. Time t
At 30, the DCT process α3 ends. DCT section 2
Processes image data continuously from block 1 to block n (final block).

Next, the processing of the quantizer 4 and the encoder 5 will be described. At time t10, the controller 8 performs the DCT processing α on the image data I of the block 1.
When the end of 1 is detected, the quantization unit 4 is instructed to start the quantization process β1A for the DCT coefficient F generated by the DCT process α1. The quantization unit 4 uses the quantization table A to perform the quantization process β1A. Quantization process β
1A is the quantization table A for the DCT coefficient F of block 1.
Is the process of quantization.

The controller 8 at time t11
When the end of the quantization processing β1A is detected, the quantization processing β1
The encoding unit 5 is instructed to start the encoding process γ1A for the quantized data R generated by A. The encoding process γ1A is an encoding process for data using the quantization table A for the block 1.

At time t12, when the encoding process γ1A is completed, the code data using the quantization table A is generated. After that, although not shown in FIG. 6,
The counter A of the coded data amount counting unit 6 (FIG. 1) counts the amount of the coded data.

As described above, the quantization process and the encoding process are performed on the quantization table A. Next, the quantization table B
For, the quantization processing and the encoding processing are performed.

The controller 8 at time t12
When the end of the encoding process γ1A is detected, the DCT process α1
Quantization process β1 for the DCT coefficient F generated by
Instruct the quantization unit 4 to start B. However, the controller 8 then instructs to perform the quantization process β1B using the quantization table B. Quantization process β1
B is a process of quantizing the DCT coefficient F of the block 1 with the quantization table B.

The controller 8 at time t13
When the end of the quantization process β1B is detected, the quantization process β1
The encoding unit 5 is instructed to start the encoding process γ1B for the quantized data R generated by B. The encoding process γ1B is an encoding process for data using the quantization table B for the block 1.

At time t14, when the coding process γ1B is completed, the code data using the quantization table B is generated. After that, although not shown in FIG. 6,
The counter B of the coded data amount counting unit 6 (FIG. 1) counts the amount of the coded data.

At time t12, the generation of code data by the quantization table A is completed, and at time t14,
The generation of the coded data by the quantization table B is completed.
Then, at time t20, the DCT process α2 ends. That is, during the DCT process α2, the quantization processes β1A and β1B and the encoding processes γ1A and γ1B are performed.
Ends.

As described above, the quantization processing and the coding processing are performed on the block 1. Next, quantization processing and encoding processing are performed on the block 2.

The controller 8 at time t20
When the end of the DCT process α2 for the image data I of the block 2 is detected, the DT generated by the DCT process α2 is detected.
The quantization unit 4 is instructed to start the quantization process β2A for the CT coefficient F. At this time, the controller 8 uses the quantization table A to instruct to perform the quantization process β2A. The quantization process β2A is a process of quantizing the DCT coefficient F of the block 2 with the quantization table A.

The controller 8 at time t21
When the end of the quantization process β2A is detected, the quantization process β2
The encoding unit 5 is instructed to start the encoding process γ2A for the quantized data R generated by A. The encoding process γ2A is an encoding process for data using the quantization table A for the block 2.

At time t22, when the encoding process γ2A is completed, the code data using the quantization table A is generated. After that, although not shown in FIG. 6,
The counter A of the coded data amount counting unit 6 (FIG. 1) counts the amount of the coded data.

Next, the quantization table B is subjected to quantization processing and coding processing. At the controller 8, the time t2
2, when the end of the encoding process γ2A is detected, D
The quantizer 4 is instructed to start the quantization process β2B for the DCT coefficient F generated by the CT process α2. However, the controller 8 then instructs to perform the quantization process β2B using the quantization table B.
The quantization process β2B is a process of quantizing the DCT coefficient F of the block 2 with the quantization table B.

The controller 8 at time t23
When the end of the quantization process β2B is detected, the quantization process β2B
The encoding unit 5 is instructed to start the encoding process γ2B for the quantized data R generated by B. The encoding process γ2B is an encoding process for data using the quantization table B for the block 2.

At time t24, when the encoding process γ2B is completed, the code data using the quantization table B is generated. After that, although not shown in FIG. 6,
The counter B of the coded data amount counting unit 6 (FIG. 1) counts the amount of the coded data.

At time t22, the generation of code data by the quantization table A is completed, and at time t24,
The generation of the coded data by the quantization table B is completed.
Then, at time t30, the DCT process α3 ends. While performing the DCT processing α3, the quantization processing β2
The A and β2B and the encoding processes γ2A and γ2B are completed.

Thereafter, similarly, the processing is continued until the last block n. The time for this series of statistical processing corresponds to the time for the DCT unit 2 to perform DCT processing for all blocks, that is, the time for a normal compression process for one time.

Although the statistical processing time is the time for one compression processing, the image compression system uses the quantization table A
It is possible to generate two types of coded data for the quantization table B and calculate the coded data amounts NA and NB.

FIG. 6 shows the timing in each processing section. Next, the processing timing for each block will be described.

FIG. 7 is a timing chart showing the timing of each block for statistical processing. The horizontal axis indicates time and corresponds to the time in FIG.

First, the processing of block 1 will be described. At time t0, DCT processing α1 for the image data of block 1 starts, and at time t10,
The CT processing α1 ends. When the DCT process α1 is completed, the code data generation process 5 using the quantization table A is performed.
1A is performed, and thereafter, code data generation processing 51B using the quantization table B is performed.

The process 51A includes a quantization process β1A using the quantization table A and an encoding process γ1A of the quantized data generated by the quantization process β1A. Processing 5
1B is a quantization process β1B using the quantization table B.
And the encoding process γ1B of the quantized data generated by the quantization process β1B.

Next, the processing of block 2 will be described. At time t10, DCT processing α2 for the image data of block 2 starts, and at time t20,
The DCT process α2 ends. When the DCT process α2 ends, the code data generation process 5 using the quantization table A 5
2A is performed, and thereafter, code data generation processing 52B using the quantization table B is performed. The process 51A includes a quantization process β2A and an encoding process γ2A. The process 51B includes a quantization process β2B and an encoding process γ2B.

As described above, since the DCT processing time is long, it is possible to perform the quantization processing and the coding processing (for example, 51A and 51B) twice during the DCT processing (for example, α2) for one block. it can. Accordingly, the statistical processing can be performed substantially twice during one compression processing for one frame image. One statistical process uses the quantization table A, and the code data amount NA
Is calculated. The other statistical processing uses the quantization table B, and the code data amount NB is calculated.

In the statistical processing, the formal scale factor SFx is obtained after calculating the code data amounts NA and NB. When the statistical processing is completed, the scale factor SFx
Is used to perform compression processing. Next, the compression process will be described. (2) Compression Processing FIG. 8 is a timing chart showing the timing at which the image compression system of FIG. 1 performs compression processing. The horizontal axis indicates time.

First, at time t50, the DCT unit 2 performs the DCT processing α1 on the image data of the block 1.
To start. At time t60, the controller 8
When the end of the DCT process α1 is detected, the DCT unit 2 is instructed to start the DCT process α2 for the image data of the block 2. Then, when the end of the DCT process α2 is detected at time t70, the DCT unit 2 is instructed to start the DCT process α3 for the image data of the block 3. At time t80, the DCT process α3 ends.
The DCT unit 2 transfers the image data from block 1 to block n.
Process continuously until.

Next, the processing of the quantizer 4 and the encoder 5 will be described. At time t60, the controller 8 performs the DCT process α1 on the image data of block 1
When the end of is detected, the quantization unit 4 is instructed to start the quantization process β1 for the DCT coefficient generated by the DCT process α1. At this time, the controller 8 gives an instruction to perform the quantization process β1 using the quantization table of the scale factor SFx.

The controller 8 at time t61
When the end of the quantization process β1 is detected, the encoding unit 5 is instructed to start the encoding process γ1 for the quantized data generated by the quantization process β1. The encoding unit 5
The generation of code data for block 1 is started. At time t62, the encoding process γ1 ends.

In the above, the quantization process and the encoding process have been performed on the block 1. Next, quantization processing and encoding processing are performed on the block 2.

The controller 8 at time t70
When the end of the DCT process α2 for the image data of the block 2 is detected, the DC generated by the DCT process α2 is detected.
Instructs the quantizer 4 to start the quantization process β2 for the T coefficient. The controller 8 then uses the quantization table of the scale factor SFx to perform the quantization process β.
Instruct to do 2.

The controller 8 at time t71
When the end of the quantization process β2 is detected, the encoding unit 5 is instructed to start the encoding process γ2 for the quantized data generated by the quantization process β2. The encoding unit 5
The generation of code data for block 2 is started. At time t72, the encoding process γ2 ends.

In the same manner, the processing is continued up to the last block n, and the image data of one frame is compressed. With that, the statistical processing and the compression processing are completed.

FIG. 9 shows the code data amount counting unit 6 of FIG.
Here is another example. The coded data amount counting unit 6 counts the coded data amount NA for the quantization table A and the coded data amount NB for the quantization table B.

The code data counting section 6 includes a counter 61
And a register A and a register B. The counter 61
Under the control of the controller 8 of FIG. 1, the amount of code data for the quantization table A and the amount of code data for the quantization table B are counted in time division.

The code data amount in block units for the quantization table A is added to the register A. When the addition for all the blocks is completed, the register A stores the code data amount NA. The coded data amount NA is the coded data amount of one frame image when the quantization table A is used.

On the other hand, the quantization table B is stored in the register B.
The code data amount for each block is added. When the addition for all the blocks is completed, the register B stores the code data amount NB. The coded data amount NB is the coded data amount of one frame image when the quantization table B is used.

FIG. 11 shows another example of the quantization table 4 of FIG. In the entire image compression system, the parts other than the quantization table 4 are the same as the configuration of FIG.

The quantization table 4 has a reference quantization table memory 11, a multiplier 12, a multiplexer 71 and a divider 15. The memory 11 stores a reference quantization table Q. The multiplexer 71 supplies either the first scale factor SFa or the second scale factor SFb to the multiplier 12 under the control of the controller 8. The multiplier 12 multiplies the reference quantization table Q and the scale factor SF to obtain Q · SFa or Q · SFb.
Is output.

The multiplexer 71 uses the scale factor S
When Fa is supplied to the multiplier 12, the divider 15 performs the following calculation and outputs the quantized data Ruv. Rounding round means rounding to the nearest integer, and DC
The T coefficient Fuv is a coefficient stored in the DCT coefficient memory 3.

Ruv = round [Fuv / (SFa · Quv)] The quantized data Ruv is encoded by the encoding unit 5, and then the code data amount NA is counted by the counter A of the code data amount counting unit 6. . The code data amount NA is the first
It is the amount of code data when compressed using the scale factor SFa.

The multiplexer 71 uses the scale factor S
When Fb is supplied to the multiplier 12, the divider 15 performs the following operation and outputs quantized data Ruv.

Ruv = round [Fuv / (SFb · Quv)] The quantized data Ruv is encoded by the encoding unit 5, and then the code data amount NB is counted by the counter B of the code data amount counting unit 6. . The code data amount NB is the second
It is the code data amount when compressed using the scale factor SFb of.

The quantizer 4 differs from the quantizer of FIG. 1 in that
It has only the memory 11 for storing the reference quantization table Q, and does not have the memory for storing the value obtained by multiplying the quantization table Q and the scale factor SF. Since the image compression system of FIG. 11 can reduce the memory capacity as compared with the image compression system of FIG. 1, the system can be downsized and the cost can be reduced.

However, the multiplexer 71 is the multiplier 12
After the scale factor SF is supplied to, the multiplier 12 performs an operation, and then Q · SFa or Q · SFb is supplied to the divider 15. That is, the calculation of the multiplier 12 delays the timing by one cushion. It is necessary to adjust the timing delay.

On the other hand, the image compression system of FIG.
Since the data read from the quantization table memory 13 or the quantization table memory 14 is directly supplied to the divider 15, it is easy to adjust the timing and there is no delay in calculation.

Next, an example in which statistical processing is performed only on sample blocks will be described. In the above-described statistical processing, processing was performed on all blocks of the image of one frame. However, since the statistical processing is only for estimating the amount of code data, it is not always necessary to perform the processing for all blocks. Therefore, processing is not performed for all blocks, but is performed only for sample blocks, so that processing time can be reduced.

FIGS. 10A to 10C show sample examples of sample blocks. The figure shows a collection of two-dimensional blocks. The hatched blocks are sample blocks.

FIG. 10A shows a checkerboard sample block. FIG. 10B shows a sample block in the form of a vertical stripe. FIG. 10C shows a horizontal stripe-shaped sample block. For example, in the case of FIG. 10A, statistical processing is performed on every other block in the order of block 1, block 3, block 5,.

These sample blocks are half the number of all blocks. If the statistical processing is performed only on these sample blocks, the time required for the statistical processing can be reduced to about half. However, the amount of code data calculated by the statistical processing is half of the amount of code data of one frame image. Considering that the code data amount is half,
It is necessary to determine the scale factor SFx.

According to this embodiment, the statistical processing can be performed substantially twice within the time of one compression processing for one frame image. The statistical processing can obtain an optimum scale factor with high speed and accuracy. The image compression system can generate coded data of a target data amount at high speed and with high accuracy.

Although the case where two kinds of quantization tables are used in the statistical processing has been described, three or more kinds of quantization tables may be used. However, it is desirable that the quantization process and the encoding process be completed within the DCT processing time for one block. Usually 2
If a quantization table of a type is used, the DCT processing can be completed within the time. By increasing the types of quantization tables used, highly accurate statistical processing, that is, a more optimal scale factor SFx can be obtained.

Also, as a method of adjusting the compression degree, a method of changing the scale factor has been described, but a method of changing the quantization table itself regardless of the scale factor or another method may be used.

The present invention has been described above with reference to the embodiments.
The present invention is not limited to these. For example, it will be apparent to those skilled in the art that various modifications, improvements, combinations, and the like can be made.

[0119]

As described above, according to the present invention,
After the DCT means finishes generating the DCT coefficient of the first block, the code data generating means generates the code data with two kinds of compression degrees, and the DCT means uses the D of the second block.
The steps of generating CT coefficients can be parallel. By performing the steps in parallel, the speed of the compression processing can be increased. Further, by generating the coded data with at least two types of compression degree, it is possible to perform a highly accurate fixed length processing.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an image compression system according to an embodiment of the present invention.

FIG. 2 is a flowchart showing a processing procedure performed by the image compression system according to the present embodiment.

FIG. 3 is a diagram illustrating an example of a reference quantization table of FIG. 1;

FIG. 4 is a diagram illustrating a method in which the code data amount counting unit of FIG. 1 counts the code data amount.

FIG. 5 is a graph for explaining processing of a scale factor determination unit in FIG. 1;

FIG. 6 is a timing chart showing the timing of each processing unit in which the image compression system performs statistical processing.

FIG. 7 is a timing chart showing timing in block units in which the image compression system performs statistical processing.

FIG. 8 is a timing chart showing a timing at which an image compression system performs a compression process.

FIG. 9 is a diagram showing another example of the coded data amount counting unit of FIG. 1.

FIG. 10 shows a sample example of a sample block. FIG.
0 (A) is a checkered sample block, FIG.
(B) is a vertical striped sample block, FIG.
(C) is a diagram showing a sample block in the form of a horizontal stripe.

FIG. 11 is a diagram showing another example of the quantization unit in FIG. 1.

[Explanation of symbols]

 1 Image Memory 2 Discrete Cosine Transform (DCT) Section 3 DCT Coefficient Memory 4 Quantization Section 5 Encoding Section 6 Code Data Amount Count Section 7 Scale Factor Determining Section 8 Controller 11 Reference Quantization Table Memory 12 Multiplier 13 Quantization Table A Memory 14 Quantization Table B Memory 15 Divider 61 Counter 71 Multiplexer

Claims (8)

[Claims] 1. An image data supply means (1) for dividing and supplying image data in block units, and a block unit for performing discrete cosine transform (DCT) on image data in block units supplied from the image data supply means. DCT means (2) for generating the DCT coefficient of each block, and each time the DCT means generates the DCT coefficient of one block, the code data is continuously generated at the first and second compression degrees for the DCT coefficient. A code data generation means (4, 5) and a counter (6) for accumulating the amount of code data for each of the first and second compression degrees generated by the code data generation means for all supplied blocks. ) And the encoded data amount of both the first and second compression degrees generated by the counter, the encoded data of the target data amount is generated. Image compression system having a compression degree estimation means (7) for estimating the degree of compression of fit. 2. The image compression system according to claim 1, further comprising means for instructing the code data generation means to generate code data at the compression degree estimated by the compression degree estimation means. 3. The code data generating means includes a quantizing means for quantizing DCT coefficients, and includes first and second code data.
3. The image compression system according to claim 1, wherein the coded data of the compression degree is generated by performing quantization using the first and second quantization tables, respectively. 4. The image according to claim 3, wherein the code data generating means includes means for generating the first and second quantization tables by multiplying the reference quantization table by the first and second scale factors. Compression system. 5. The image compression system according to claim 3, wherein the code data generation unit includes a coding unit that performs Huffman coding after quantizing the DCT coefficient. 6. The DCT means is means for continuously DCTing image data in block units, and the code data generating means before completing DCT of image data for a block in which the DCT means is present. 1
The image compression system according to any one of claims 1 to 5, wherein the generation of code data of both the first and second compression degrees for the immediately preceding block is completed. 7. The image compression system according to claim 1, wherein the image data supply means supplies image data for a sample block in an image of one frame. 8. A step of generating a DCT coefficient of the first block by performing a discrete cosine transform (DCT) on the image data of the first block, and when the generation of the DCT coefficient of the first block is completed,
DCT the image data of the second block to generate the DCT coefficient of the second block, and when the generation of the DCT coefficient of the first block is completed,
By the time the generation of the DCT coefficient of the second block is completed, the first DCT coefficient of the first block
Generating the code data with the second compression degree and subsequently generating the code data with the second compression degree.