JP3132836B2 - Image data compression / decompression method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for handling image data, and more particularly to an image data compression / decompression system for compressing and restoring image data in storage and transmission of image data.

[0002]

2. Description of the Related Art In recent years, with the development of OA and the improvement of the processing technology of a CPU, a database of color or monochrome gradation image information and the like has been increasingly used by a computer. The data amount of such image information is several Mbytes per sheet (one screen), which is very large. Therefore, in order to efficiently handle image information in storage and transmission, data compression is conventionally performed to reduce the data amount.

[0003] There are various methods for data compression, one of which is universal coding. The present invention can be applied not only to character code compression but also to various types of data. In the following, one word unit of data, which is referred to in the field of information theory for description, is a character, and data in which a plurality of words are connected is described. Call it a character string.

As a typical method of the above-mentioned universal coding, there is a Ziv-Lempel code (for example, for example, Munakata "Ziv-Lempel Data Compression Method", Information Processing, Vol.
6, No. 1, 1985). In this Ziv-Lempel code, universal type and incremental decomposition type (Incrementa
l parsing) have been proposed.

[0005] The universal type algorithm has a feature that although a large amount of calculation is required, a high compression ratio can be obtained. This method is a method of dividing encoded data from an arbitrary position in a past data sequence into a sequence having a maximum length that matches (subsequence), and encoding as a copy of the past sequence.
As shown in FIG. 5, a P buffer and a Q buffer are provided, and the P buffer stores encoded input data.
The data to be encoded is stored in the buffer. Then, the sequence of the Q buffer searches the sequence of the P buffer,
Find the matching maximum length substring in the P buffer. Then, a set of information for designating the maximum subsequence is encoded in the P buffer.

Further, there is an LZSS code as an improvement of the universal algorithm. (TCBell, “Better OPM
/ L Text Compression ”, IEEE Trans. On Commun., Vo
l. COM-34, No. 12, Dec. 1986). In this LZSS code, as shown in FIG. 4A, the start position of the maximum matching sequence in the P buffer is obtained, and a set of matching lengths and the next symbol are discriminated from each other by a flag, and the coding is performed in the smaller code amount. is there.

On the other hand, the incremental decomposition type algorithm is inferior to the universal type in the compression ratio, but has a feature that it is simple and easy to calculate. Incremental decomposition type Ziv-Lempel
In the code, a sequence of input symbols is represented by x = aabababa
If a is assumed, the incremental decomposition into the component sequence x = X ₀ X ₁ X ₂ ... is performed as follows. First, let X _{1 be} the longest column from which the rightmost symbol of the existing component has been removed, X = a · a
b, aba, b, aa,... Therefore, X ₀
= Λ (empty column), X ₁ = X ₀ a, X ₂ = X ₁ b, X ₃ = X
_{2 a, X 4 = X 0} b, X 5 = X 1 a, can be decomposed .....

As shown in FIG. 4B, each of the component sequences that have been incrementally decomposed is coded by using the component index and the next symbol in the order in which the components are arranged as shown in FIG. 4B. In other words, the incremental decomposition type algorithm obtains a coding pattern that is conscious of the maximum length of the subsequences decomposed in the past, and encodes them as a copy of the subsequences decomposed in the past.

Further, as an improvement of the above-mentioned incremental decomposition type algorithm, there is an LZW code. (TA Welch, “A Te
chnique for High-Performance Data Compression ”, Co
mputer, June 1984). In the LZW code, the next symbol is incorporated in the next subsequence so that it can be coded using only indexes.

FIG. 6 is a flowchart showing the processing by the conventional LZW coding. In the LZW encoding process, a rewritable dictionary is provided, the input character string is divided into different character strings (substrings), and the character strings are registered in the dictionary with reference numbers in the order in which the character strings appear, and are registered in the dictionary. In this case, the character string is represented by the reference number of the longest matching character string registered in the dictionary and encoded.

First, in step S1, a character string consisting of one character for every character is registered in a dictionary in advance as an initial value, and then encoding described below is started. In addition, the dictionary is searched with the first character K input to find the minimum number ω, and this is set as the initial character string. Subsequently, in step S2, the next character K of the input data is read, and in step S3, it is checked whether all character inputs have been completed. When the input character exists, that is, when the character K exists (Y), it is determined whether or not the dictionary has a character string (ωK) obtained by adding the character K read in the process S2 to the initial character string ω.

If the character string (ωK) does not exist in the dictionary in the discrimination processing S4 (N), the reference number ω of the character K obtained in the processing 1 in the processing S6 is output as a code word code (ω),
Further, the character string (ωK) is registered in the dictionary as a new reference number, the input character K in the processing S2 is replaced with the reference number ω, the dictionary address n is incremented, and the processing is executed again from the processing S2.

On the other hand, when the character string (ωK) exists in the dictionary in the processing S4 (Y), the character string (ωK) is replaced with the reference number ω (S5), and the processing returns to the processing S2 again. The search for the maximum matching length is continued until the character string ωK cannot be searched from the dictionary.

When it is determined that the character K does not exist in the determination process S3 (N), a code S is executed by a process S7.
(Ω) is output and the processing ends (END). The above-described processing will be specifically described with reference to FIGS.

The input data INPUT SYMBOLS of FIG. 8 is read sequentially from left to right. When the first character a is entered, there is no matching character string other than a in the dictionary, so OUTPUT CODE 1
(Reference number ω) is output as a codeword. Then, the extended character string ab is assigned a reference number 4 and registered in the dictionary.
The actual dictionary registration is performed as a character string 1b as shown on the right side (ALTERNATE TABLE) in FIG. Subsequently, the second character b becomes the head of the character string. Since there is no matching character other than b in the dictionary, reference number 2 is output as a code character. At the same time, since the expanded character string ba is not in the dictionary, the character string ba is represented by 2a. register. Then, the third "a" becomes the head of the next character string. Hereinafter, this process is similarly continued.

FIG. 7 is a flowchart of a process of decoding the compressed data obtained by the decoding process of FIG. In the LZW decoding process in FIG. 7, similarly to the encoding, a character string consisting of one character for all characters is registered in the dictionary as an initial value before decoding starts.

First, the first code (reference number) in step S11
Is read, and the current CODE is set to OLDcode. Since the first code corresponds to one of the reference numbers of one character already registered in the dictionary, a character code (k) that matches the input code CODE is searched for, and Output. Note that the output character K is FINcha for later exception processing.
Set to r.

Next, in step S12, the next code is read and CODE is set as INcode. Subsequently, it is determined whether or not there is a new code (S13), and when there is no new code (N), the process ends (END). Also, when it exists (Y), the code CODE input in process S13
It is checked whether or not is defined in the dictionary (S1
4). Since the code word normally input has been registered in the dictionary in the previous processing, the character string code (ωK) corresponding to the code CODE is subsequently read from the dictionary, the character K is temporarily stacked (S16), and the reference number code (Ω) is set as a new code CODE, and is executed again from step S15.
The steps S15 and S16 are recursively repeated until the reference number ω reaches one character K. Finally, in step S17, the characters stacked in step S16 are LIFO (Last In Fa).
(St Out) Output in pop-up format. At the same time, a new reference number is registered in the dictionary as a new reference number in a character string represented by a combination (ω, K) of the code ω used last time and the first character K of the character string restored this time.

The LZW decoding process will be specifically described with reference to FIG. First input code (INPUT COD
E) is 1, and the characters a, b, and c are already registered in the dictionary as reference numbers 1, 2, and 3 as shown in FIG. Therefore, it is replaced with the character string a of the reference number that matches the code 1 by referring to the dictionary and output. The same applies to the next code 2, which is replaced with the character b and output. At this time, a new reference number 4 is added to the character string (1b) obtained by combining the code 1 processed the previous time and the first character b decoded this time, and registered in the dictionary.

The third code 4 outputs the character string ab by replacing the character string 1b obtained by the dictionary search with the character string ab. At the same time, a character string 2a (= b) obtained by combining the code 2 processed last time and the first character a of the character string decoded this time
Add a new reference number 5 to a) and register it in the dictionary. Then, decoding is performed by repeating the same operation.

In the LZW decoding of FIG. 10, there is the following exception processing. This exception processing occurs, for example, when the sixth input code 8 is decoded. The decryption 8 is not defined in the dictionary at the time of decryption and cannot be decrypted. In this case, a character string 5b is obtained by adding the first character b of the character string ba decoded last time to the code 5 processed last time, and an exceptional process of replacing 2ab = bab and outputting the result is performed. Then, after outputting the character string, the reference number 8 is added to the character string 5b obtained by adding the first character b of the character string decoded this time to the previous code 5 and registered in the dictionary.

This exception processing is performed in steps S4 and S8 of the decoding processing flow of FIG. 6. Finally, in step S7, the output of the character string and the registration in the dictionary in which the reference number is added to the new character string are performed. Will be

In the LZW decoding of FIGS. 7 and 10, a case has been described where a dictionary is created in real time while decoding the code on the decoding side, but the dictionary created at the time of encoding is decoded as it is. By copying and using it,
In some cases, decryption is performed, and in this case, exception processing on the decryption side becomes unnecessary.

[0024]

The above-mentioned conventional universal code is a reversible coding method, and is suitable for compressing data such as character codes and object codes. Conventionally, this universal code has been used for compressing image data. However, since the compression rate is low, a higher compression rate is required. Especially in image data, there is a condition that the restored image does not need to completely match the original image, that is, a condition different from the compression of character codes and object codes. There is a demand for a new data system.

It is an object of the present invention to provide an image data compression system which performs a specific process before a lossless encoding system and effectively compresses image data.

[0026]

SUMMARY OF THE INVENTION The present invention relates to a system for irreversibly compressing and restoring image data. According to the present invention, the input image data is subjected to density conversion, that is, image reduction, and the number of dots forming the image is reduced. Then, data compression is performed by a universal coding method or the like. This data compressed image information is decoded by the same decoding method, that is, the universal coding method, and then density conversion is performed to obtain the same density as the original image data.

At the time of transferring data and the like, not only is the image density converted, but also the data is further compressed, so that the amount of information can be reduced. In addition, since the obtained data is restored and density conversion is performed, image information that is partially different from the original image but is close to the original image can be obtained, and the capacity of the image data during transmission and storage can be reduced.

[0028]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below in detail with reference to the drawings. FIG. 1 is a configuration diagram of a compression system according to an embodiment of the present invention.
The conversion rate is added to the density conversion specifying unit 11. The conversion rate is a signal that specifies the dot density to be converted in the X and Y directions. The density conversion specifying unit 11 stores this conversion rate and outputs it to the density conversion unit 12.

The image data is applied to the density conversion unit 12, and converts the input image data in accordance with the conversion rates in the X and Y directions specified by the density conversion designating unit 11. If the density conversion in the density conversion unit 12 is, for example, to reduce the image data to 縦 in both the vertical and horizontal directions, the density conversion is performed as dot data representing the upper right point in the image of the 2 × 2 dot area. . Note that the present invention is not limited to this.

The image data whose density has been converted by the density converter 12 is applied to a universal encoder 13 where it is compressed according to the universal encoding method and output as compressed data. Data output as compressed data has an information amount of, for example, 2 × before universal encoding.
If the compression is 2, the density has been converted to 1/4, so that the compression becomes higher as a whole.

The compressed data compressed by the compression method of the first embodiment is stored in an auxiliary storage device such as a magnetic disk device. Alternatively, it is transmitted to another device by a communication means such as a line.

On the other hand, if the compressed data is not restored, image data cannot be obtained. FIG. 2 is a configuration diagram of the restoration method according to the embodiment of the present invention. The compressed data transmitted via a line or read from the storage device is applied to the universal conversion unit 15. The universal decoding unit 15 is a circuit for universal decoding, and decodes in a predetermined state, that is, in the same state as the state at the time of compression, that is, in the same method. The data obtained by the universal encoding and decoding is lossless data, and the result of the density conversion by the density conversion unit 12 is output by the universal decoding unit 15. This output is image data with reduced density.

On the other hand, the conversion ratio (X
Direction, Y direction), and the density conversion designation unit 17 stores the conversion rate and outputs the conversion rate to the density conversion unit 16. The density conversion unit 16 expands the density by conversion in the direction opposite to the reduction by the density conversion at the time of compression, that is, the reduction at the time of compression, and outputs it as image data. That is, the density conversion unit 16 sets the same density as the original image density.

FIG. 3 is a data flow diagram in the binary image data compression / decompression method of the present invention. The input image data G1 is reduced and converted by the density converter 12 (G
2). This reduction conversion is a mere reduction in which the reduction ratio is 共 に in both the vertical and horizontal directions. The value is reflected depending on whether the upper right dot of the 2 × 2 dots on the screen is white or black, and the other values are converted. Perform the operation of thinning out the dots. As a result, reduced converted image data G2 is obtained. This information is universally encoded and stored or transmitted as compressed data. When restoration is performed, for example, when reproduction is performed on the receiving side, universal decoding is performed first, and the reduced converted image data G2 is reproduced (G3). The restored image data G3 is the same image data as the reduced converted image data G2. This is because lossless compression is used in universal encoding and the like.

The restored image data 13 is added to the density converter 16 and converted. This conversion rate is the reciprocal of the reduction, that is, the enlargement rate 2, and the dots, that is, the dots of the restored image data, are enlarged so that all the black or white values are 2 × 2 dots (G4).

By compressing data after performing the above-described reduction, and conversely decoding and expanding the data, image data can be stored and transmitted with a small amount of information.
Also, the compressed data is much smaller than the original image data, and the compression efficiency can be increased.

In the embodiment of the present invention, when the image data is reduced, the image data is thinned out.
Although this is reflected on individual dots, the present invention is not limited to this. For example, in the reduction, white and black may be averaged with respect to the number of dots such as 2 × 2 dots, and black may be set when there are many blacks, and white may be set when there are many whites. It is also possible to set a value depending on the adjacent dot.

[0038]

As described above, according to the present invention, it is possible to obtain an image data compression / decompression method for effectively compressing image data or the like for which information need not be completely stored.

[Brief description of the drawings]

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

FIG. 2 is a configuration diagram of a restoration method according to an embodiment of the present invention.

FIG. 3 is a flowchart of a process of the present method.

FIG. 4 is an algorithm of universal encoding.

FIG. 5 is a principle diagram of encoding of a universal ZL code.

FIG. 6 is a flowchart of a conventional LZW encoding process.

FIG. 7 is a flowchart of a conventional LZW decoding process.

FIG. 8 is an explanatory diagram of LZW encoding.

FIG. 9 is an explanatory diagram of a dictionary configuration example.

FIG. 10 is an explanatory diagram of LZW decoding.

[Explanation of symbols]

 11 Density conversion designation unit 12 Density conversion unit 13 Universal encoding unit

────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Yasuhiko Nakano 1015 Uedanaka, Nakahara-ku, Kawasaki City, Kanagawa Prefecture Inside Fujitsu Limited (56) References JP-A-63-292769 (JP, A) JP-A-2-246578 (JP, A) JP-A-2-188076 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) H04N 1/38-1/393 G06T 3/40

Claims (5)

(57) [Claims] 1. A method for compressing and restoring image data, comprising compressing image data subjected to density conversion as input image data for encoding, decoding encoded data, performing density conversion, and restoring. 2. A density conversion to be performed before encoding is input.
2. The image data compression / decompression method according to claim 1, wherein the pixel density is converted in accordance with the first conversion rate. 3. The method of claim 2, wherein the density conversion after decoding is performed by
3. The image data compression / decompression method according to claim 1, wherein the pixel density conversion is performed in accordance with the conversion rate of the image data. Wherein said compression is any one of claims 1 to 3, characterized in that a compression by universal coding
2. The image data compression / decompression method according to 1 . Wherein said decoding restored by the universal coding
Any one of claims 1 to 4, characterized in that it is No.
2. The image data compression / decompression method according to 1 .