JPH01146485A - Picture information processor - Google Patents

Abstract

PURPOSE:To perform high-resolution-vector-encoding for a picture at high frequencies and to perform the encoding with large number of gradations for the picture at the low frequencies by utilizing the correlation of the pictures, classifying their categories, and applicably dividing bands. CONSTITUTION:The Hadamard transformation of a 4X4 matrix is carried out by a orthogonal converting part 3. By inspecting the power, etc., of a prescribed sequency component in a block by a category classifying part 4, the said picture block is classified into one of the 8 categories. A scalar quantizing part 5 band-divides an input sequency component according to inputted category classification data into the prescribed sequency component at the low frequencies and the prescribed sequency component at the high frequencies, the small number of the vectors is added to the category of the low frequencies and the large number of the vectors is added to the category of the high frequencies, the picture at the higher frequencies is made to obtain a resolution, and the picture at the lower frequencies is made to obtain the gradation.

Description

[Detailed Description of the Invention] [Industrial Application Field] The present invention relates to an image information processing device, and in particular, to image information processing that orthogonally transforms digital image data block by block, vector quantizes the generated vector data, and encodes the image information. It relates to a processing device.

[Prior Art] In recent years, with the development of device technology represented by VLSIs and the like, the image data that is the subject of data transmission or data storage is shifting from conventional binary images to multi-value images. In addition, the degree of integration of image reading sensors has increased, and the resolution has also increased, resulting in an enormous amount of image data, and it is desired to realize a high-speed, highly efficient encoder. In the past, several reports have been made regarding the encoding of multivalued images. For example, predictive coding,
There are block coding, orthogonal transform coding, compression coding using vector quantization, etc. Recently, vector quantization methods that provide high compression rates have been attracting attention. However, in order to perform vector quantization, training data for determining a predetermined number of reproduction vectors is required, and highly correlated data can achieve a high compression rate. Therefore, in order to effectively perform vector quantization, a method has been devised in which an image is orthogonally transformed block by block, and the correlation of the transformed images is utilized to perform vector quantization.

Usually, each component in the block after transformation is used as a vector element, and vector quantization is performed. However, vector quantization includes the following problems.

That is, as the number of vector dimensions increases, the amount of calculation required to obtain the optimal reproduction vector becomes enormous. In addition, the amount of calculation required to select the optimal reproduction vector for an image is enormous, and in order to implement this in hardware, a huge amount of LUT (Look up Table) is required.
)is necessary.

Conventionally, it has been proposed to divide vector elements within a block into a plurality of frequency bands, reduce the number of vector dimensions, and perform vector quantization for each band. However, this band division is fixed, and for example, the first
Bands were divided by Schaming bands as shown in Figure 0. In the figure, the orthogonal transform coefficients are F (u,
v), it is divided so that u+v=const. The figure shows a case where orthogonal transformation is performed on a 4×4 pixel block, which is divided into vectors v1 to v7 by band division. However, such fixed division may result in inconvenient division depending on the nature of the image block. For example, in an image block with a low frequency, the power of vector v7 or V6 is extremely small. Furthermore, in images with directionality (for example, line drawings), the power of each element in a vector is extremely uneven, which is inconvenient for vector quantization.

[Problems to be Solved by the Invention] The present invention eliminates the above-mentioned drawbacks of the prior art, and its purpose is to perform efficient vector encoding while well preserving the characteristic properties of image blocks. An object of the present invention is to provide an image information processing device that performs the following steps.

[Means for Solving the Problems] In order to achieve the above object, the image information processing apparatus of the present invention includes a blocking means that divides digital image data into M×N pixel units, and a block image data processing unit that divides digital image data into units of M×N pixels. a transformation means for generating frequency-analyzed vector data of a predetermined number of dimensions by performing orthogonal transformation on the block image; The present invention further comprises: a classification means for classifying into categories, a masking means for masking predetermined vector data in a high frequency range within a block according to the classified category, and a quantization means for performing vector quantization on the masked remaining vector data. The outline is as follows.

Preferably, the masking means masks more high-frequency vector data for the image block classified into the low-frequency category by the classification means, and masks more high-frequency vector data for the image block classified into the high-frequency category. One aspect of this is masking a small amount of vector data.

Preferably, the quantization means further divides the masked remaining vector data into a plurality of groups according to frequency, and reduces the number of dimensions for the vector data in the low frequency band, and reduces the number of dimensions for the vector data in the high frequency band. One aspect of this is to divide the data to increase the number of dimensions.

Preferably, one aspect of the quantization means is to perform band division of different band patterns depending on the category of the classification means.

[Operation] In this configuration, the blocking means divides the input digital image data into M×N pixel units. The transformation means generates vector data of a predetermined number of dimensions subjected to frequency analysis by performing orthogonal transformation such as Hadamard transform on the divided block image data.The zero classification means generates vector data of a predetermined number of dimensions from the generated vector data. The properties of the block image are classified into one of a plurality of categories based on the properties of the block image. The masking means masks predetermined vector data in the high range within the block according to the classified category. Preferably, the masking means masks more high-frequency vector data for image blocks that the classification means has classified into the low-frequency category;
Furthermore, for image blocks classified into the high frequency category, vector data with less high frequency is masked. Then, the quantization means performs vector quantization on the masked remaining vector data. Preferably,
The quantization means further divides the masked remaining vector data into multiple groups according to frequency, reducing the number of dimensions for the vector data in the low frequency band and increasing the number of dimensions for the vector data in the high frequency band. Divide it as follows. Preferably, the quantization means performs band division into different band patterns depending on the category of the classification means.

[Description of Embodiments] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

First, the present invention will be explained in detail. First, the present invention is based on the following idea. Highly correlated information sources such as images have the property that power concentrates in low frequencies when frequency analysis is performed. Utilizing this property, efficient encoding is performed by converting the image block by block into the frequency domain and particularly encoding the low frequency components.

However, in order to perform frequency analysis and obtain sufficient correlation, the transform block size must be large to some extent. Therefore, if vector quantization is performed on the transformed block as it is, the LUT (lookup table) for vector quantization will become enormous.

Therefore, the present invention solves this problem based on the second idea described below. That is, using image correlation, after orthogonal transformation, each block is classified into categories based on the magnitude of frequency. Furthermore, the number of vector dimensions is reduced by adaptively dividing images within a category into bands and vector quantizing each. That is, the number of LUTs in hardware is reduced by category classification and band division, and the orthogonal transform components required for each type of image are vector quantized by performing band division according to the characteristics of the image.

In the present invention, the blocks after orthogonal transformation are classified into categories based on the magnitude of frequency.

By giving a small number of vectors to the low frequency category and a large number of vectors to the high frequency category, high frequency images can obtain high resolution, and low frequency images can have high gradation. make sure you get it. Furthermore, by performing band division, the dimension of the vector is reduced and the efficiency of vector quantization is increased. In band division, by making the vector order of the low frequency band smaller than the vector order of the high frequency band, vector quantization is performed that weights the low frequency component, thereby obtaining coding efficiency that cannot be obtained with conventional methods.

FIG. 1 is a block diagram of an image information processing apparatus according to an embodiment. In the figure, reference numeral 1 denotes an image input device, and in this embodiment, digital image data with a density of 8 bits and a resolution of 16 pel/mm is manually input using a raster scan method. Reference numeral 2 denotes a line buffer, which stores four lines of digital image data manually input along each reading scanning line, and sequentially outputs block image data of 4×4 pixels in parallel.

3 is an orthogonal transform unit, which performs Hadamard transform of a 4×4 matrix in this embodiment. FIG. 2(A) shows the orthogonal transform unit 3
Block image data XI, X2,...
, X+a (8 bits each), as shown in Figure 2 (B).
is vector data (sequence component) Y after Hadamard transformation by the orthogonal transformation unit 3 , , Y 12+ ...
, YaaC (10 bits each), and FIG. 3 shows a two-dimensional Walsh type transformation pattern employed in the Hadamard transformation of this embodiment. Returning to FIG. 1, 4 is a category classification unit, which classifies the image block into one of eight categories (eight characteristic properties of the image block) by examining the power, etc. of a predetermined sequence component within the block. Classify. Further, the category classification unit 4 extracts the DC component Y11 from among the sequence components Y-th to Y44 in the block and outputs it as is.

5 is a scalar quantization unit, in which sequence components Y12 to Y44 excluding the DC components Y and 1 among the intra-block sequence components Yll to Y44 are manually generated. According to the conventional method,
The 15 sequence components Y12 to Y44 are scalar quantized with an appropriate number of bits to form 15-dimensional vector elements, and these are vector quantized all at once. However, this requires a huge look-up table (LUT) and is not practical. Information sources with high correlation, such as general images, have the property that power is concentrated in low frequency components when frequency analysis is performed. Utilizing this property, efficient encoding can be achieved by converting an image block by block into the frequency domain and encoding with emphasis on low frequency components. However, in order to obtain sufficient correlation through frequency analysis, the block size of the orthogonal transform must be increased to some extent. Therefore, the number of dimensions of the vector data generated by the conversion increases, and if vector quantization is performed all at once, the LUT will become extremely large. Therefore, the scalar quantization unit 5 of this embodiment converts the input sequence component Y1 according to the input category classification data.
2 to Y44 are divided into bands into a predetermined sequence component with a low frequency (low sequence component) and a predetermined sequence component with a high frequency (high sequence component), and scalar quantization is performed using a specific number of bits for each band, and the quantization is performed. The low-sequence vector elements and high-sequence vector elements are supplied to a separately provided vector quantization unit (LUT).

Reference numeral 6 denotes a vector quantization unit (L), which vector quantizes low-sequence vector elements formed according to category classification data. 7 is a vector quantization unit (H);
Similarly, high sequence vector elements formed according to category classification data are vector quantized. To perform vector quantization in this embodiment, 256 fi each of reproduction low sequence vector elements and reproduction high sequence vector elements are determined for each category based on training sequences performed on multiple types of representative images in advance. put. Therefore, each of these 256 types of reproduction vectors is associated with each 8-bit vector quantization code data in advance. Therefore, vector quantization section (L) 6 and vector quantization section (H) 7
By comparing each input low sequence vector element or high sequence vector element with each of the 256 types of reproduced low sequence vector elements or reproduced high sequence vector elements obtained in advance from the training sequence, the amount of distortion is reduced. A lookup table (LUT) that selects 8-bit vector quantization code data with the relationship
) is memorized. In addition, since the high sequence vector element or low sequence vector element of this embodiment has different sequence components for each category as described later, the vector quantizer (L) 6 and the vector quantizer (H) 7
Category classification code data is manually inputted from the category classification section 4 via the signal line 4-2, so that one of the different LUTs prepared for each category is used. In this way, the predetermined sequence vector within the block is converted into vector quantization code data of 8 bits each by the vector quantization section (L) 6 and the vector quantization section (H) 7,
It is output to signal lines 6-1 and 7-1.

Reference numeral 8 denotes an integrated vector quantization unit, which integrates each 8-bit vector quantized code data obtained by vector-quantizing the low-sequence vector element and the high-sequence vector element, respectively, and converts it into a total of 10 bits of integrated code data. .

9 is a code data storage unit which stores the upper 8 bits of the sequence component Y++ (DC component) of the signal line 4-1 and the signal line 4-1.
-2 category classification code 3 bits and signal line 8-1
Record the integrated quantized 10-bit integrated code data.

FIG. 4 is a diagram showing the information structure of the block encoding code of the embodiment. In the figure, the block encoding code consists of a total of 21 bits, the 8 bits from the MSB are data for the sequence component Y++ (average value), the next 3 bits are category classification code data, and the next 10 bits are predetermined bits other than the Y-th bit. This is integrated code data obtained by vector encoding sequence components. In this way, digital image data of (4×4) pixels×8 bits=128 bits per block is compressed into fixed length 21-bit block encoded code data, and the data compression rate is approximately 1/6.

FIG. 5(A) is a diagram explaining the basic concept of the category classification method of the embodiment. The category classification in this embodiment is based on the power of a predetermined sequence component with low frequency.When various block image data of a general image is Hadamard-transformed and its frequency components are examined, it is found that the edge part of the image has low power. It concentrates on the components and its absolute value is large.

In this example, by utilizing this property, the edge amount ED is calculated as follows: ED; l YI21 + l YI31 + I Y2
It is defined as 11 + I Y221 + I Y311.

FIG. 6(A) is a diagram specifically showing the category classification method of the embodiment. Three threshold values Tl<T2<
Set T3, and in descending order of ED, if ED≦T1, the flat part, if Tl<ED≦T2, the halftone flat part, and if T2<
When ED≦T3, it is classified as a weak edge portion, and when T3<ED, it is classified as a strong edge portion. Furthermore, since there are vertical, horizontal, and diagonal edge patterns in the weak edge portion and the strong edge portion, there are a total of eight classifications as shown in FIG. 6(A).

FIGS. 5(B) and 5(C) show a method of classifying weak edge portions and strong edge portions. For images with strong edges in the vertical direction,
The power in the shaded area in FIG. 5(B) increases, and in an image with strong edges in the horizontal direction, the power in the shaded area in FIG. 5(C) increases. In this embodiment, by utilizing such properties, the following vertical edge amount VE and horizontal edge amount HE are defined.

V E = ly+zl+ly+sl+ly+41HE
= 1Y211+1Y311+1Y411 Using these, the weak edge and strong edge portions are further classified into three categories. That is, IVEI-1) IEI>El or IVEI ≧IHEI, then IVEl-IH
If EI > El or IVEI < IHEI, it is classified as a horizontal edge, and in other cases, it is classified as a diagonal edge. However, E
l is a constant. Letting El be a constant at a weak edge and E2 be a constant at a strong edge, parameters E1 and E2 are determined so that El<E2.

The shaded portions in FIG. 6(A) are the sequence components remaining without being masked according to each category. These are the fifth
It is shown in positional correspondence with each sequence component in the figure. In FIG. 6A, the sequence components in the shaded area are further divided into bands into low frequency components (shaded area) and high sequence components (shaded area). The numbers in parentheses are the number of dimensions when these are considered as low sequence vector elements and high sequence vector elements.

In the flat area category, low sequence vector element Y1□
, Y21. There is only 3 dimensions of Y2□, which is 102
The data is encoded into one of four types (10 bits) of vector quantization code data. In categories other than flat areas, there are low sequence vector elements and high sequence vector elements, each of which is encoded into one of 256 different types (8 bits) of vector quantization code data. As shown in the figure, in this embodiment, the stronger the edge component, the greater the number of dimensions of the sequence vector. This is to preserve resolution.

On the other hand, in flat areas, the number of gradations is increased and saved.

FIG. 6(B) is a diagram showing the bit allocation of scalar quantization for low sequence components and high sequence components defined for each category in the embodiment.

The total of each of the low sequence vector and high sequence vector after quantization is fixed at 18 bits. The number of vector dimensions is smaller for low-sequence vectors than for high-sequence vectors, and by increasing the number of bits allocated to sequence components with lower frequencies, priority is given to the gradation of low-frequency components of the image. encoding is performed.

FIG. 7 is a block diagram showing extracted vector quantization means of the embodiment. In the figure, the low sequence vector and high sequence vector divided into bands for each category by the scalar quantization unit 5 are manually processed into a vector quantization unit (L) 6 and a vector quantization unit (H) 7, each with 18 bits. Here, each of the 256 fl is encoded into 8-bit vector quantization code data. At the same time, 3-bit category classification code data is input, and each vector quantizer 6.7 can be constructed of an LUT that receives 21-bit input and outputs 8-bit quantized code data.

FIG. 8 is a diagram showing the operational concept of the integrated vector quantizer 8 of the embodiment. In the figure, Ll.

L2. ..., L256 is vector quantization code data of a low sequence vector, Hl, H2゜...,
H2Se is vector quantization code data of a high sequence vector. Therefore, the number of combinations of each vector quantization code data of these low sequence vectors and high sequence vectors is generally 256 fffi or more. However, considering the correlation of general images, there is a correlation between low sequence vectors and high sequence vectors, so it is normal that there are not all possible combinations, that is, 256x256=65536. Therefore, we perform training on sample images in advance to find possible combinations, such as those between the upper and middle rows of Figure 8, and under conditions such that the number of combinations falls within 1024 types (10 bits).
Further vector quantization is performed by grouping together those with similar distortions,
A new integrated (combination) vector quantization code (10 bits) from 01 to C1024 is created. The configuration to achieve this is to create a 10-bit integrated vector quantization code using a vector quantization method from each 8-bit low sequence vector quantization code data, high sequence vector quantization code data, and 3-bit category classification code data. It consists of LUTs that generate data.

Next, a method of determining the reproduction vector in the embodiment will be explained.

In this embodiment, a plurality of sample images having different frequencies are scanned and read in advance, and an optimal reproduction vector is determined by the well-known LBG method for each category classification and each band division described in FIGS. 5 and 6.

That is, a training sequence is performed in the same manner as described with reference to FIGS. 1 to 8 to obtain a predetermined code length (21 bits in this embodiment). The big difference is that there are no hardware restrictions on the training sequence. In reality, the optimal solution for the reproduction vector is determined by computer simulation. Therefore, after orthogonally transforming the sample image data, scalar quantization as explained in Fig. 6(B) is not performed, but sequence vector elements for each category are extracted as explained in Fig. 6(A). Then, this is divided into bands into low sequence vectors and high sequence vectors, and an optimal reproduction vector is determined by the LBG method. In this way, the scalar quantization error shown in FIG. 6(B) is prevented from affecting the optimal reproduction vector design.

In addition, when designing the optimal reproduction vector, the parameter TI for category classification based on the edge amount ED explained in FIG. 6(A)
, T2. T3 is determined as follows. T1 in FIG. 6(A). T2. When determining the four classifications based on T3, multiple sample images are used, and the number of 4×4 pixel blocks belonging to each is calculated as follows: When category flatness ED≦T1 B1 When category halftone dot flatness TI<ED≦T2 B2 When category weak edge T2<ED≦T3 B3 When category strong edge ED>T3 B4 Then, parameters TI, T2. Determine T3. This method is used to prevent the Euclidean space of vectors from becoming abnormally large as edges become stronger when vector quantization is performed for each category. In other words, since the number of reproduction vectors for each category is fixed, it is possible to prevent the error between the reproduction vector for the human image from becoming too large as the edge becomes a category, and to prevent the error between the reproduction vector and the human image from becoming too large. This is for the purpose of preventing a reproduction vector that is extremely biased toward a flat area from being determined as an optimal solution because of the large number of faces.

FIG. 9 is a flowchart showing how to obtain a reproduction vector in the embodiment. In the figure, in step S1, a sample image is cut out into blocks of 4×4 pixels. In step S2, the block is orthogonally transformed (Hadamard transform in this embodiment).

In step S3, the sequence data YIl after orthogonal transformation is
~Y44 classifies each block into categories based on the edge amount ED. In step S4, each category is divided into bands as shown in FIG. 6(A), and low sequence vectors and high sequence vectors are extracted. Step S
In step 5, the steps from step 51 to step S4 are performed until all sample images (in this example, several representative images with different frequencies) have been scanned, and the low sequence vector and high sequence vector are scanned for each category. Create a table of all sequence vector sample vectors. Step S
In step 6, for all the sample low sequence vectors and sample high sequence vectors obtained in steps S31 to S5, for each category, both the low sequence vector and the high sequence vector are 256 by the LBG method.
Find the optimal vector of @. In step S7, for each category, the low-sequence vector quantization code data and the high-sequence vector quantization code data are integrated by the integrated vector quantization described in FIG. 8 to create 1024 types of integrated vector quantization code data. do. The reproduction vector of this embodiment is determined by the above steps S1 to S7.

[Other Embodiments] In the above-mentioned embodiments, Hadamard transform is used as the frequency analysis means, but the present invention is not limited to this. Other orthogonal transformations such as cosine transformation and KL transformation may also be used.

[Effects of the Invention] As described above, according to the present invention, the correlation between images is used to perform categorization and band division adaptively, so high-resolution vector encoding is performed on high-frequency images. , low-frequency images can be encoded with a large number of gradations.

[Brief explanation of the drawing]

FIG. 1 is a block diagram of the image information processing apparatus according to the embodiment, and FIG. 2(A) shows block image data X1. X2. ..., Diagram showing x16, Figure 2 (B
) is the vector data (sequence component) after Hadamard transformation by the orthogonal transformation unit 3 Y+++Y12+ ..., Y
44, FIG. 3 is a diagram showing the two-dimensional Walsh type transformation pattern adopted in the Hadamard transformation of this embodiment, and FIG.
The figure shows the information structure of the block encoding code of the embodiment. Figure 5 (A) is a diagram explaining the basic concept of the category classification method of the embodiment. Figure 5 (B) and (C) are the weak edge FIG. 6(A) is a diagram specifically showing the category classification method of the embodiment, and FIG. 6(B) is a diagram showing the low sequence components and strong edge portions specified for each category of the embodiment. A diagram showing the bit allocation of scalar quantization for high-sequence components, FIG. 7 is a block configuration diagram extracting and showing the vector universalization means of the embodiment, and FIG. 8 is the operation of the integrated vector quantization unit 8 of the embodiment. FIG. 9 is a flowchart showing how to obtain a reproduction vector in the embodiment. FIG. 10 is a diagram showing an example of band division by bands in conventional SchaIQing. In the figure, 1... image input device, 2... line buffer, 3... orthogonal transformation unit, 4... category classification unit, 5...
... Scalar quantization section, 6... Vector quantization section (L)
,? . . . vector quantization section (H), 8 . . . integrated vector quantization section, 9 . . . code data storage section. (A) (B) Figure 2 Figure 3 (A) (B) (C) Figure 5 Weak knee・′；Needle
Yowani Zo horizontal (3,51 (3,6)
(3,5) Lade Shishi Otsuko ε
Five archers...S A+me
Chemical Engineering・15; Section 6
Figure (B)

Claims (4)

[Claims] (1) Blocking means for dividing digital image data into M×N pixel units; Transforming means for generating frequency-analyzed vector data of a predetermined number of dimensions by performing orthogonal transformation on the divided block image data; , a classification means for classifying the block image into one of a plurality of categories based on a predetermined vector element of the generated vector data; and a predetermined vector data of high range within the block according to the classified category. An image information processing apparatus comprising: a masking means for masking; and a quantization means for performing vector quantization on the masked remaining vector data. (2) The masking means masks more high-frequency vector data for the image block classified by the classification means into the low-frequency category, and masks less high-frequency vector data for the image block classified into the high-frequency category. The image information processing apparatus according to claim 1, wherein vector data is masked. (3) The quantization means further divides the masked remaining vector data into multiple groups according to frequency, reducing the number of dimensions for the vector data in the low band, and reducing the number of dimensions for the vector data in the high band. 3. The image information processing apparatus according to claim 2, wherein the image information processing apparatus is divided into a large number of images. (4) The image information processing apparatus according to claim 3, wherein the quantization means performs band division of different band patterns depending on the category of the classification means.