Method for image compression during fractal coding

FIELD: information technology.

SUBSTANCE: in the method, after using a modified LBP technique, calculation of LBP code values and search for equivalent LBP code values are performed in rank and domain regions, formed by the same number of pixels located on a circle, where the radius of the circle of the domain region is greater than the radius of the circle of the rank region; the number of pixels, the radius of the circle and the coordinates of the position of the centre pixel for the rank and domain regions are stored.

EFFECT: faster encoding through selection of image characteristics which describe the domain and rank regions.

4 dwg

 

The invention relates to the field of image processing for the storage and transmission of digital information and can be used in fractal image encoding.

Fractal image coding is one of the most promising methods of data compression with losses currently. Fractal coding is characterized as a high compression ratio and high speed image reconstruction. The compression ratio of this algorithm can be compared with known modern methods of coding, such as JPEG, and the speed of image reconstruction is much higher. The mathematical description of the image as the result of fractal coding allows to reconstruct an image of any size without any significant large-scale distortions that are inherent in other methods of encoding. Fractal coding is very promising for computer multimedia applications and digital video. The main drawback is the large computational complexity of the step of encoding image [1].

Fractal image coding involves finding the set contractive transformations that display domain blocks in a variety of rank blocks. Rank-based units cover the entire image, can be the same size, but more likely to use adapt�Noah split with variable block size.

Consider the basic algorithm of fractal image encoding [2]. The image I is divided into rectangular rank blocks Riand rank the blocks do not overlap and cover the entire image. Further, the cover image I is a set of domain blocks (Di. Domain blocks may overlap, have different size, and their number can reach hundreds or thousands of units. For each rank of the block Rifind contractive affine transformation withat best approximates a given rank block domain block Di.

The affine transformation includes rotation, compression, scaling, shearing, reflection. To an affine transformation was compressive, the size of the blast area should not exceed the dimensions rank. The parameters of this transformation remain for further restoration of rank of the block. Good compression depends on the ability to find a good correspondence between the rank of domain blocks. Search for the best correspondence between rank and domain blocks requires the most computational effort.

The closest in technical essence to the claimed method of image compression with fractal coding is a method for processing images using LBP method [3].

This procedure is proposed to use Izv�STN method local binary patterns LBP (Local Binary Patterns) [4]. The LBP algorithm of the method is as follows. Allocate a 3x3 neighborhood around each pixel. Neighboring pixels that have a value above or equal to the value of the Central pixel is assigned the value 1, and those pixels whose value is less than the value of the Central pixel is assigned the value 0. Eight binary digits associated with eight adjacent pixels, are read sequentially in a clockwise direction to form a binary number. These binary numbers or decimal equivalent can be associated with the Central numbers and used as characteristics of the local texture.

In this position of the pixel (xc, yc) LBP code is defined as an ordered set of binary comparisons of pixel intensity between the Central pixel and eight neighboring pixels.

The decimal form of the resulting 8-bit word - LBP code of a pixel with coordinates (xc, yc) is expressed by a formula:

where inthe intensity value of the neighboring pixel with index n;

ic- the intensity value of the Central pixel (xc, yc);

s(x) is a threshold function, which is defined as follows:

Due to the distinctive properties of the texture and very low computational cost LBP method is �effective method in the analysis of image texture.

Prototype method involves reduction of the original image and calculates its LBP code, obtaining a new matrix of values of the LBP code the size of the reduced image. Due to the fact that the second matrix is associated with a reduced original image, the number of LBP code selected in them, is an area of the image is greater than the value of the first matrix in the original image. This matrix represents the domain field.

The disadvantage of this method is that the LBP code is calculated for rank and domain areas with the same dimensions, and derived features that describe the data area, do not have sufficient accuracy.

The technical result, the aim of the invention is to provide a method for increasing the speed of the fractal encoding of images. Method of image compression is to highlight the characteristics that describe a domain and rank the field, for comparison, domain and ranking of areas based on these characteristics, rather than individual pixels, which significantly reduces the amount of work.

The task is carried out by applying the modified method LBP, in which the pixels chosen at some distance from the Central pixel and the coordinates P(xp, yp) is determined as follows:

where xp, yp- the coordinates of the selected pixels;

xc, yc- the coordinates of the Central pixel;

R is the radius of the circle which is formed of the selected pixels;

R - the number of pixels on the circle.

After calculating the values of the LBP code rank area with a certain radius R and the number of points P take to calculate the domain field the same number of points P, but with a radius large radius that was used to compute the LBP code rank area, and each value of the matrix LBP code domain area is a larger area than the matrix value LBP code rank area. However, some adjacent pixels have different meanings LBP code that affects its sensitivity to rotation and choice of the first neighboring pixel (Fig. 4.).

View values consistently perform in a clockwise direction, whichever is the starting pixel, depends, what are binary numbers and their decimal equivalents are formed. In this case, we can take the maximum, minimum or median value of the LBP code to make this code is invariant to rotation.

Further, for any value of the LBP code of the first matrix are equivalent to the second value. So find a similar area in both images that represent rank and domain�th region and the correspondence between them. Do this for all values of LBP code of the first matrix and store the coordinates of the second matrix, where is the number equivalent to the value of the first matrix, and the position of the Central pixel.

The inventive method is illustrated by drawings:

Fig.1 - displays the rank of domain areas in the region;

Fig.2 - the calculation of the LBP code;

Fig.3 - modified LBP code;

Fig.4 - options for the development of LBP code.

Experimental application of the proposed method of image compression using the method of LBP can significantly reduce the time and computational costs for fractal image coding. The method is simple, effective and versatile.

Sources of information

1. Barnsley M., L. Hurd Fractal Image Compression. - Wellesley, MA: A. K. Peters, Ltd, 1993.

2. C. Welstead. Fractals and wavelets for image compression in action. Training manual. - M.: Publishing house of Triumph, 2003, 320 p.;

3. Zykov A. N., Kartsov S. K., D. Y. Kupriyanov image Processing method LBP (Local Binary Patterns). News, MSIU No. 3 (27), 2012, p. 42.

4. Ojala T., Pietikainen, M., Maenpaa, T. Multi resolution gray-scale and rotation invariant texture classification with local binary patterns. - IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol.24. N7. Jul 2002. P. 971-987.

Method of image compression with fractal coding, which includes the image received at the input of the encoder divides the image into many non-overlapping rank areas calculating values of LBP codes, the creation of domain areas, the creation of matrices of values of LBP codes, search in the domain matrix of the values of LBP codes, equivalent to LBP codes of rank of a matrix, characterized in that the modified method of applying LBP, calculating values of LBP codes and search for equivalent values of LBP codes perform in rank and domain fields formed by the same number of pixels located on the circumference, and the radius of the circle domain region is greater than the radius of the circle rank region, and stores the number of pixels, the radius of the circle, the coordinates position of the Central pixel for rank and domain areas.



 

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