Method for alphabetical image representation

FIELD: information technology.

SUBSTANCE: method for alphabetical representation of images includes a step for primary conversion of an input image to a multi-centre scanning (MCS) format, constructed according to rules of a plane-filling curve (PFC). The initial MSC cell is a discrete square consisting of nine cells (3×3=9), having its own centre and its own four faces (sides). Scanning of the initial MSC cell is performed from the centre to the edge of the square while bypassing the other cells on a circle. The path with a bypass direction to the left from the centre of the square and then on a circle, clockwise, is the priority path for scanning and displaying images.

EFFECT: high efficiency of encoding images.

3 cl, 5 dwg

 

The technical field

The invention relates to the field of digital image processing.

The method is designed for direct representation (for example, directly from the sensor) input image into semantic units of type:

- areas of constant brightness;

traces of direct and/or boundary contours;

- chaotic clusters of pixels or noise.

This representation of the image, then Fas-form, can effectively implement a procedure of image processing in all their classes (in parentheses indicate the most representative subclasses):

analysis (recognition, filtering),

- conversion (compression, revision),

- synthesis (animation, quality improvement),

- communication with management, recovery of the signal at the receiver.

Efficiency is defined by three parameters:

- performance by eliminating the preliminary procedures

- easy hardware implementation by creating a uniform and parallel circuits processing areas of the image with the managed schema decrease the uncertainty of the cell Heisenberg for a pair of "Plane - alphabet as the essence of amplitudes",

- create a measure of semantic similarity in the image plane, and between multiple images by identifying and eliminating redundancies of different types (syntactic, geome the historical, interplanar, interframe and so on).

The method has the hardware and software implementation.

The prior art. Analogues and their disadvantages

In [1, p.114] and in [2, p.153] shows the problem of creating a common alphabet for bitmap images, which is not achievable because of design limitations on the length of the alphabet. Indeed, in this case, the size of the alphabet contains, even for color images, up to 224or 16.78 million characters, which is a non-enum. In [3, str] specified alphabet, consisting of 48 letters and portrait-oriented Recognizer. Disadvantage: the input image is monochrome; the lack of alphabet letters forming the noise; no control threshold of uncertainty of Heisenberg, i.e. the slice plane to the point of area of interest

Prototypes

Creating absolute alphabet representation of digital images on today is problematic due to its bitmap representation, i.e. it is not possible to create the alphabet [1,p.114]. However, in this plan there are developments focused on specialized systems [3], in this case we are talking about truncated scripts.

The level of technology

The invention relates to the field of image processing. Its application - view images into semantic units in the rate of scanning, i.e. without the use of tools predobrabotki and, in the form of the alphabet with a corresponding schema formation. This level of development allows us to abandon the concept of "Pixel", i.e. to withdraw from the procedures of image processing all local operators and translate them in global.

Description of the invention

The invention relates to the field of image processing and can be used for semantic image processing. The claimed method differs by the presence of the alphabet image and mechanism of word formation. To get alphabet image, you must perform the following steps:

Step 1. Conversion of the input, for example, a bitmap image in the ICR form.

Step 2. Performing a component analysis of the image to monochrome, grayscale, color.

Step 3. The decomposition of the ICR form in the gray code.

Step 4. Getting alphabet for each plane in the rules Heisenberg.

Step 1. Getting the ICR form. The method of conversion and image processing on the basis of mnogoznachnoi scanner (ICR), built according to the rules of the curve that fills a plane (CSP), i.e. by setting the start and direction of recursion, is determined by the fact that the initial ICR cell (the beginning of the recursion) is a discrete square consisting of nine cells (3×3=9), with its own centre and its four faces (sides); the initial scan of the ICR cell (in the direction of recursion) starts from the center is and to the edge of the square and then over the rest of the cells in a circle (thus 16 possible ways traversal of accounting 8 boundary cells and two types of bypass - clockwise and counterclockwise).

Priority, including scanning and image visualization is way over to the left of center and then circle clockwise (figure 1).

This construction is called facet or pFas, where R is the recursive step, when p=1 have described above is the starting cell(3×3=9).

For the formation direction of recursion we will distinguish four types of bypass (figure 2)needed to describe 2Fas:

- previously described crawl w1 as the initial (1Fas1);

bypass w2 as a mirror from 1Fas1 to the left side (1Fas2),

bypass w3 as a mirror from 1Fas2 in the upper side (1Fas3),

bypass w4 as a mirror from 1Fas3 in the right direction.

To obtain recursions ICR built 2Fas (figure 3) (p=2, with a side of 9 cells, ie 9×9=81)responsible for the direction of the recursion, where the source is primary 1Fas, which (on the basis of the above rotations w) is the sequence with the initial movement of the square to the left of 1Fas and then clockwise around 1Fas or path: w1 w2 w3 w4 w3 w2 w3 w4 w3. In fact it generates the direction of the recursion.

Each successive recursion pFas (R>2), based on 1Fas and 2Fas. Example for 3Fas given in figure 4.

The inclusion of recursions ICR based 1Fas and 2Fas (R>2) allows the plane to represent the coordinates of rotation (w), the carrier of which is pFas, and the point of this plane, thenQmi> xypis a square of side 3pwhere p=0...N, where N, previously assigned this square, which is nested in the size of the original image. When p=0 (OFas) or pointQxy0degenerates in the pixel, when R>0, which operates the ICR plane is divided intoQxyp-1independent pixels (squares). The parameters Hu for Q is its center on the image plane.

Thus ICR creates a 6-coordinate space point Q:

two coordinates of a Cartesian dimension (x, y coordinates),

- coordinate measuring rotation (w),

- coordinate measuring nesting point in the tree Q,

- coordinate dimension of the faces of the facet or pFas,

- coordinate measuring rooms storage Qpxyin the memory of the computer or F11.

Moreover, these six coordinates of the measurement can be combined depending on the assigned tasks.

Total for 6-coordinate is the mapping of the Cartesian plane in the memory, when one-to-one mapping pointQxyp/msubsup> in Fpor formally:Qxyp...Fpwhen p=0, Qxy->RHu; where PHupixel with Cartesian coordinates, and Fp- cut in integers as fa...fa+pforQxypin memory of the transmitter, fa- number ofQxypor centre number on the plane.

An important advantage of the present method is that the image represented by the ICR, the allocation of space segment the image according to the specified criterion relevance (i.e. the information value for an observer) in the image plane (including the allocation of direct lines, areas of constant brightness or color, chaotic clusters of pixels and others) perform in the form of managed investment pFas by setting R=1...10 in point of area of interest relevant, with precision p, using the representation of a point of interest in the image based on the conversion table obtained according to claim 1 (1Fas or 2Fas or pFas). And all of this does not require the use of equations months is nahozdenija point on a physical level representation of the image through its dimensions.

Thereby perform direct access (including multiple processors independently) to the point of interest or groups of points of interest of the image according to scheme 9pwhere each processor runs its own "tree" from above "forest", thus access is the minimum width of 9 pixels1or, depending on the parameter R=1...10. In this case, the processors are homogeneous and their number depends on the size of the input image.

In the present method effectively determine the 8-connected neighborhood of any point of the zone of interest in the image, by using mapping tables (without masking means): this 8-connected neighborhood is defined for theQxyp. When p=0 it turns into a point, and when R>0, it turns into the neighborhood of a square with side 3p. Moreover, the neighborhood presents faces of the square based on the conversion tables 1Fas and 2Fas, without taking into account the dimensions of the image.

We present a method, by default, performs image compression without loss. The path (track) scan set from him (square) of the centre under the rules of the curves fill the plane (CSP); when p=1 (form CSP), image, regardless of its size (w,h), is segmented into 9 squares, each of which CE is mentirosa again on 9 squares or p=2 (direction CSP). Other CSP (R>2) are constructed recursively on the length of p=0...10. When p=10, the size of the image or w,h equal 59049 pixels. Formally, mnogoserijnaya scan (ICR) performs a one-to-one mapping xi, yj in rj, where the pair xi, yj - a Cartesian coordinates of a pixel or Rhu village, it is, rj is the number of natural numbers cut 1...59049×59049.

Step 2. Component analysis of the image to monochrome, grayscale, color. We believe that any image, even color, consists of regions having different components. The objective of this step is to identify and fix these areas pointQxyp. The analysis is conducted for a set of pixels lying on pFas. Pets its quantization with prescribed accuracy.

Step 3. The decomposition of the ICR form in the gray code.

The image provided by the ICR, laid out on a plane in the rules of reflective gray code. We then obtain a single plane for monochrome, eight planes for grayscale and 24 planes for color images, each plane is defined by the alphabet. The decomposition is performed according to the following scheme:

Let A - eight-bit integer representing subpixel image (component R, G or color image, or the brightness value Y for grayscale image is agene). Define one-to-one mapping In=In(A) as follows:

where ai(bi) is the i-th bit of the source (output) number, and the first bit is the most senior, the eighth - youngest;

the plus sign in the circle" means the operation of addition modulo 2, the streak is over, including the operation of bit inversion.

This mapping specifies the representation of a number in gray code obtained from the eight-bit binary reflective gray code by bitwise inversion. Inverse mapping a=a () is defined as follows:

a1=b1;a2=b1b2;a1=b1...b1,i3,8

The results of the direct conversion=(A) (gray code) and the inverse transformation A=A(In) (gray code) for all eight-bit numbers declareroles is in the form of two 256-element table, which are used to convert the numbers in the program.

Next, the image provided by the ICR, laid out on a plane using gray code, receiving a single plane for monochrome, eight planes for grayscale and 24 planes for color images; as a result, each plane is represented by the alphabet, the letter which represents ways to fill a square 3×3; thus each letter becomes the target according to the rules, Frege, when the denotation of the letters is her ikonika, concept of letter is it a sign letters have her number or number; created the alphabet semantically presents the following three subsets: CDs (media of constant brightness), regularity (traces direct), chaotic clusters of pixels with different dispersions.

Step 4. Getting alphabet for each plane in the rules Heisenberg.

Note that the elements of the alphabet and their placement on specific areas form element coverage in amplitude and spatial plane, also called the cell Heisenberg, which shows the concentration of the basic letters of the alphabet inQxyppoint. Typically, the cell Heisenberg set in the time-frequency plane [4, str]. However, due to the special features of the device means the visual perception of the image by an observer of type "Person", choose the amplitude screening. The standard scheme of the experiment: "Great see in the distance.", i.e. the frequency component of this storyline is not acceptable, simply have no possibility of convergence to self-similarity. A further development is the transformation, where each image plane is based on the alphabet, which is set for squares ICR with a value of R=1-9 by building a vector of characters with the 9pletters, i.e. the length of the alphabet - 512 letters, based on the rate of scanning, form a geometric basis in the rules of convergence to reduce the uncertainty principle of Heisenberg, but in contrast to existing algorithms that implement this rule, let the pyramid of Mallat (spatial-frequency field), it uses a pair of space - the alphabet as the essence of amplitudes with different metric convergence and spatial (plane) pyramid (pFas) is known as the pyramid of the amplitudes (pJ scale) leading, with convergence on pFas (plane) has a base 9, and the convergence by pJ has a base 4, which provides direct navigation processor to semantic units (relevant). Rule Heisenberg development (expansion) of each pointQxypthe image plane is constructed by setting J (values of the brightness component of the real image for a length of 512), identifying dominant relevancy over the alphabet on the square 9pwith subsequent verification of its contents according to the criteria of redundancy forQxyp(isolated straight line, arc, contour line, constant brightness, chaos and so on), through a quantitative calculation of the elements of the alphabet on the square 9p. When image compression is performed by eliminating the interplanar redundancy between i-th and i+1, i-1-th planes by creating a timeline of their mutual similarity faceted least for grayscale and color images. On the basis of pFas is implemented threshold selection scheme contours on monochrome, grayscale and color images by comparing pFas between planes and between the components (RGB) assessment according to the scale Levenshtein [5, p.44]. Full alphabet for any digital image is given (figure 5).

Brief description of drawings

Figure 1 shows the initial ICR cell, called 1Fas, which is a discrete square consisting of nine cells (3×3=9), with its own centre and its four faces (sides); scan ICR initial cell starts from the center over to the left from the center and then around the circle clockwise.

Figure 2 shows four types of traversal square of nine cells (3×39): bypass w1 described as primary (1Fas1); bypass w2 as a mirror from 1Fas1 to the left side (1Fas2), bypassing the w3 as a mirror from 1Fas2 in the upper side (1Fas3), bypassing w4 as a mirror from 1Fas3 to the right side; these types of traversal required to obtain directions recursion ICR for 2Fas.

Figure 3 shows the direction of the recursion ICR for 2Fas (p=2, with a side of 9 cells, 9×9=81), where source is the initial 1Fas, on the basis of the above rotations w in sequence with the movement of the square to the left of 1Fas and then clockwise around 1Fas: w1 w2 w3 w4 w3 w2 w3 w4 w3.

Figure 4 shows the recursion ICR for 3Fas (p=3, with a side of 27 cells).

Figure 5 shows the complete alphabet, built according to IDC.

Industrial applicability

View digital images of the alphabet, then Fas form:

to obtain the initial semantic basis of the image without preprocessing, i.e. the rate of scanning, thereby reducing the processing time up to 80%;

to make effective speed alternatives to type operator Sobel and Converter type have, using threshold schemes allocation, allowing in the first case, go to global operators, the second cut;

- implement the attitude of tolerance to existing algorithms, especially for frequency representations of the image (DCT, DWT);

to perform image compression (lossy or not) by Sigma the operation on relevancy (region constant brightness line and chaotic clusters) with subsequent transfer of the last relevant, let the one-dimensional DCT;

The method focuses on hardware and software implementation.

Sources of information

1. Dsolomon. Data compression, image and sound. -M.: Technosphere, 2004.

2. Roubicon, Refriger and other Digital conversion of images M: "Hot line - Telecom", 2003.

3. Ustin. The recognition of images. -M.: Nauka, 1970.

4. Gonzales, Rwuser image processing. -M.: Technosphere, 2006.

5. Tahanan. Self-organizing maps.- M.: BINOM. Knowledge laboratory, 2008.

1. The way the alphabetical representation of images, characterized in that the input image is initially converted into the format mnogoznachnoi scanner (ICR), built according to the rules of the curve that fills a plane (CSP), i.e. by setting the start and direction of recursion, where the initial ICR cell represents a discrete square consisting of nine cells (3×3=9), with its own centre and its four faces (sides); the initial scan cell ICR perform from the center to the edge of the square and then over the rest of the cells in a circle, with priority for scanning and image visualization is the way to go with the direction of traversal to the left from the center of the square and further on a circle, clockwise; denote this construction by facet pFas, where p - step p is kursiy, when p=1 are as described above, the initial cell (3×3=9); to build further recursions distinguish four types of crawl: the previously described crawl w1 as the initial (1Fas1), bypassing w2 as a mirror from 1Fas1 to the left side (1Fas2), bypassing the w3 as a mirror from 1Fas2 in the upper side (1Fas3), bypassing w4 as a mirror from 1Fas3 in the right direction; to get directions recursions ICR, i.e. 2Fas (p=2, with a side of 9 cells, 9×9=81), where the beginning is 1Fas (on the basis of the above rotations w), crawls in sequence with the initial movement of the square to the left of 1Fas and then clockwise around 1Fas: w1 w2 w3 w4 w3 w2 w3 w4 w3; all subsequent ICR build recursions pFas (R>2) build on the basis of 1Fas and 2Fas; next, the image provided by the ICR laid out on a plane using gray code, receiving a single plane for monochrome, eight planes for grayscale and 24-plane color image; as a result, each plane is represented by one-dimensional alphabet; alphabet organized as a set of options for filling the square 3×3; thus, each letter is formed according to the rules Gfree when the denotation of the letters is her ikonika, concept of letter is it a sign letters have her number or number; created the alphabet semantically represent the following three subsets: compact media of constant intensity, regularity traces of the por is activated, chaos - clusters of pixels with different dispersions.

2. The method according to claim 1, characterized in that each pointQxypthe image plane strout parameter pJ (luminance values under the actual image on the length of 512), by identifying the dominant relevancy over the alphabet on the square 9pwith subsequent verification of its contents according to the criteria of redundancy forQxyp(isolated straight line, arc, contour line, constant brightness, chaos) by quantitative calculation of elements of the alphabet on the square 9p.

3. The method according to claim 1, characterized in that the further compression of the image performed by eliminating the interplanar redundancy between i-th and i+1, i-1-th planes by creating a timeline of their mutual similarity faceted least for grayscale and color images.



 

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3 cl, 8 dwg

FIELD: radio engineering, communication.

SUBSTANCE: method of encoding transform coefficients includes: encoding the position and value of a last non-zero coefficient of a block; encoding at least one coefficient in accordance with a first coding mode if the amplitude of said at least one coefficient is less than or equal to a threshold; and determining a cumulative sum of amplitudes of previously coded non-zero coefficients that are greater than the threshold; and if the cumulative sum is less than a cumulative threshold value, and the position of the last non-zero coefficient is less than a location threshold: coding a subsequent coefficient in accordance with the first coding mode; otherwise, coding a subsequent coefficient in accordance with a second coding mode.

EFFECT: high encoding efficiency.

22 cl, 8 dwg

FIELD: information technology.

SUBSTANCE: methods and systems for processing document object models (DOM) and processing video content are provided. Information content which is represented by a DOM and which includes a scripting language associated with the information content is received and original content of the DOM is stored after execution of the scripting language. Further, video content is adapted for client devices. The scripting language associated with the information content can be sent to client device along with a modified DOM and processed video content. Pre-processing of the scripting language is carried out to identify nodes related to video content and to maintain all other original nodes, for example.

EFFECT: easier processing of video data.

23 cl, 12 dwg

FIELD: physics, computer technology.

SUBSTANCE: invention relates to display devices. The device has a mechanism for obtaining graphic data, which generates frame data from input graphic data, a frame buffer control unit which determines if the connected display device has a frame buffer. If the connected display device has a frame buffer, said control unit bypasses the operation of storing frame data in a local frame buffer and transmits the frame data to the connected display device.

EFFECT: high efficiency of the device for processing graphic data owing to use of a remote frame buffer.

20 cl, 8 dwg

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