Real-time video coding/decoding

1. A method of encoding, a digitized sequence of video frames in a multi-core system, the method comprising:

performing core motion estimation;

determining weighted texture prediction use from a difference between normalized density functions for luminance histograms of an original frame and of a reference frame; and

calculating parameters for weighted texture prediction;

wherein the core motion estimation comprises:

dividing a current video frame and a reference video frame into blocks of pixels;

defining in the current frame an initial block B(F, X INIT , Y INIT ) with coordinates X INIT , Y INIT ;

selecting in the reference frame a plurality of N candidate blocks with coordinates (X j , Y j ) where j=0, 1 . . . N, using a predetermined criterion; and

performing core motion estimation for the current frame based on similarity between pixels of a block of the current frame and pixels of blocks of the reference frame;

wherein the motion estimation comprises:

calculating values of a function of distortion Q(F, R, X j , Y j ) for all N candidate blocks; and

comparing minimum values of Q(F, R, X j , Y j ) with predetermined thresholds to find coordinates for motion estimation X j =X MIN , Y j =Y MIN from the function of distortion having a lowest value: Q MIN =Q(F, R, X MIN , Y MIN ).

2. The method of claim 1 , wherein the motion estimation algorithm comprises:

calculating Q(F,R, X MIN +j*Dx MIN ,Y MIN +j*Dy MIN ) for increasing j until inequality (I) is true, or a boundary of the frame is reached:

Q ( F,R,X MIN +j*Dx MIN ,Y MIN +j*Dy MIN )<= Q ( F,R,X MIN +( j+ 1)* Dx MIN ,Y MIN +( j+ 1)* Dy MIN )  (I)

 where j=0, 1, 2 . . . J, and J− is minimum nonnegative integer value for which the inequality (I) is true; Dx and Dy are steps in horizontal and vertical direction;

defining parameters X MIN =X MIN +J*Dx MIN , Y MIN =Y MIN +J*Dy MIN ;

calculating Q MIN =Q(F, R, X MIN , Y MIN );

finding one resulting coordinate pair X MIN , Y MIN for Q MIN =Q(F,R, X MIN ,Y MIN ) from the following coordinate pairs:

(X MIN +Dx MIN ,Y MIN +Dy MIN );

(X MIN −Dx MIN ,Y MIN +Dy MIN );

(X MIN +Dx MIN ,Y MIN −Dy MIN ), and

(X MIN −Dx MIN ,Y MIN −Dy MIN ).

3. A method of encoding a digitized sequence of video frames in a multi-core system, comprising distributed motion estimation for encoding successive frames using one reference frame and the core motion estimation algorithm of claim 2 , wherein the distributed motion estimation comprises:

reducing frame size using reduction coefficient in each spatial dimension;

calculating and storing a plurality of spatially reduced frames;

selecting reduced block size in reduced frames as 8×8;

calculating and storing motion vector sets for reduced frame pairs using the core motion estimation algorithm;

up-scaling the motion vectors for each block according to the reduction coefficients in each dimension and defining a rough full-pel motion vector for each block of an up-scaled frame;

calculating a full-pel motion vector by applying the core motion estimation algorithm to the rough full-pel motion vector as an additional candidate, and reducing search range according to the reduction coefficients; and

calculating a quarter-pel motion vector for each block by applying the core motion estimation algorithm to the full-pel motion vector as a starting point and a search range of 2.

4. A method of encoding a digitized sequence of video frames in a multi-core system, the method comprising:

performing core motion estimation;

determining weighted texture prediction use from a difference between normalized density functions for luminance histograms of an original frame and of a reference frame; and

calculating parameters for weighted texture prediction;

wherein the weighted texture prediction comprises:

performing estimate for normalized density functions for histogram H o of the original frame and histogram H r of the reference frame;

comparing the difference between the density function for H o and the density function H r with a predetermined threshold, and determining that the weighted prediction is to be applied if the difference is less than the threshold; and

calculating weighted prediction parameters A and B for at least one block of pixels P(x, y):

A*L(x+mx,y+my)+B:

A=So/Sr ; and

B=Mo−A*Mr;

where L(x, y) is luminance of the reference frame;

(mx, my) is motion vector for the block P(x, y);

So is deviation for the original frame;

Sr is deviation for the reference frame;

Mo is mean value for the original frame; and

Mr is mean value for the reference frame.

5. A method of encoding a digitized sequence of video frames in a multi-core system, the method comprising:

performing core motion estimation;

determining weighted texture prediction use from a difference between normalized density functions for luminance histograms of an original frame and of a reference frame; and

calculating parameters for weighted texture prediction;

wherein the method further comprises the step of:

pre-processing temporal denoising, including:

providing motion estimation by creating a texture prediction frame for a current frame from a previous frame and a corresponding motion vector;

performing deblocking of the prediction frame; and

modifying the current frame based on at least one pixel of the current frame and at least one pixel of the prediction frame:

I=I +sign( I−P )· F (abs( I−P )),

where:

I is the pixel of the current frame,

P is same position pixel of the prediction frame after deblocking,

abs is the absolute value function,

sign is a sign function (sign(x)=−1, if x<0, sign(x)=0, if x=0, and sign(x)=1, if x>0), and

F is a discrete time function.

6. The method of claim 5 , wherein the discrete time function has the following properties:

F(D) is sufficiently smooth inside interval [0; T 2 ];

F (0)=0;

F ( D )≧ D/ 2 and F ( D )≦ D , if Dε[ 0 ;T 0 );

F ( D )= D/ 2, if Dε[T 0 ;T 1 ); and

F ( D )≦ D/ 2 and F ( D )≧0, if Dε[T 1 ;T 2 );

where T 0 , T 1 and T 2 are predetermined parameters which control strength of noise suppression, 0≦T 0 <T 1 <T 2 .

7. The method according to claim 6 , wherein the de-noising is not performed if a motion vector function mv_cost exceeds a pre-defined threshold.