A color transformation method of transforming a first color component set representing a first color space to a second color component set representing a second color space is provided. The method comprising: reading the first color component set; transforming the read first color component set to the second color component set using a predetermined transformation function; and storing the second color component set to correspond to the first color component set, wherein the transformation function is defined by: defining a first transformation matrix used for acquiring dominant components of the first color component set and multiplying each of the elements of the determined first transformation matrix by a predetermined integer k. The method further comprises inversetransforming the second color component set to the first color component set.

A color transformation method of transforming a color component set representing color space to another color component set representing different color space, the method comprising:
a) reading a first color component set from a predetermined memory;
b) transforming the read first color component set to a second color component set using a predetermined transformation function; and
c) storing in tangible memory the second color component set to correspond to the first color component set, wherein the predetermined transformation function is defined by:
determining a first transformation matrix for acquiring dominant components of the first color component set; and
multiplying each element of the determined first transformation matrix by a predetermined integer k,
thereby transforming a color component set representing color space to another color component set representing color space.

The method of claim 1, wherein the method further comprises:
d) inversetransforming the second color component set to the first color component set, the inversetransforming comprising:
d1) reading the second color component set from the predetermined memory;
d2) inversetransforming the read second color component set to the first color component set by using an inverse transforming function; and
d3) storing the transformed second color component set to correspond to the first color component set, wherein the inverse transformation function is defined by:
determining an inverse matrix of the first transformation matrix, and
multiplying each of the elements of the inverse matrix by a reciprocal of the integer k.
 The method of claim 2, wherein the integer k satisfies k=2^{m}, wherein m is a positive integer.
 The method of claim 3, wherein the first transformation matrix is determined based on one of a Discrete Fourier Transformation (DFT), a Discrete Cosine Transformation (DCT), a Walsh Transformation, and a Hadamard Transformation.
 The method of claim 3, wherein the first transformation matrix is determined based on a KarhunenLoeve (KL) Transformation used for acquiring dominant components using an autocorrelation characteristic of the first color components.

The method of claim 5, wherein the first transformation matrix is determined by:
e1) calculating an autocorrelation matrix of normalized values of elements of the first color component set;
e2) calculating an eigenvector by KLtransforming the autocorrelation matrix;
e3) compensating the eigenvector to substantially equalize a dynamic range of the first color component set with a dynamic range of the second color component set; and
e4) compensating a bias of the first color component set with a bias of the second color component set.
 The method of claim 6, wherein the first color space is a color space represented by a RGB color component set, the compensating operation e3) further comprises normalizing each of the elements of the eigenvector using an L1 norm, and the transformation function is substantially equal to the following matrix: [0.6460.6880.6661.00.2120.7880.3221.00.678].
 The method of claim 1, wherein the multiplying comprises multiplying each element by a same predetermined integer.

A color transformation method transforming a color component set representing a color space to another color component set representing a different color space, the method comprising:
a) reading a first color component set from a predetermined memory;
b) transforming the read first color component set to a second color component set using a predetermined transformation function; and
c) storing the second color component set to correspond to the first color component set, wherein the transformation function is defined by:
c1) determining a first transformation matrix used for acquiring dominant components of the first color component set, and
c2) compensating the first transformation matrix to substantially equalize a dynamic range of the first color component set with a dynamic range of the second color component set, thereby transforming a color component set representing a color space to another color component set representing a different color space.

The method of claim 9, further comprising:
d) inversetransforming the second color component set to the first color component set, said inversetransforming comprising:
d1) reading the second color component set from the predetermined memory;
d2) inversetransforming the read second color component set to the first color component set using an inverse transformation function; and
d3) storing the transformed second color component set to correspond to the first color component set, wherein the inverse transformation function is determined by defining an inverse matrix of the first transformation matrix.
 The method of claim 10, wherein the first transformation matrix is determined based on a KarhunenLoeve (KL) Transformation used for acquiring dominant components using an autocorrelation characteristic of the first color components.

The method of claim 11, wherein the first transformation matrix is determined by:
e1) calculating an autocorrelation matrix of normalized values of elements of the first color component set and
e2) calculating an eigenvector by KLtransforming the autocorrelation matrix, wherein the transformation matrix is compensated to substantially equalize a bias of the first color component set with a bias of the second color component set.
 The method of claim 12, wherein the first color space is a color space represented by a RGB color component set, the compensating operation c2) further comprises normalizing each of the elements of the eigenvector using a L1 norm, and the transformation function is substantially equal to the following matrix: [0.6460.6880.6661.00.2120.7880.3221.00.678].

A color transformation apparatus transforming a color component set representing a color space to another color component set representing a different color space, comprising:
tangible memory for storing a first color component set and a second color component set to correspond to each other; and
a color transformer for transforming the first color component set read from the tangible memory to the second color component set, wherein the color transformer comprises:
a dominant component acquirer for determining a first transformation matrix used for acquiring dominant components of the first color component set according to a predetermined transformation algorithm;
a first multiplier for calculating a second transformation matrix which corresponds to the determined first transformation matrix multiplied by a predetermined integer k; and
a central processor for calculating a second color component set using the second transformation matrix.

The apparatus of claim 14, further comprising an inverse transformer for inversetransforming the second color component set to the first color component set by using an inverse transformation function, wherein the inversetransformer comprises:
an inverse matrix calculator for calculating an inverse matrix of the first transformation matrix and
a second multiplier for calculating an inverse transformation matrix by multiplying each of the elements of the inverse matrix by a reciprocal of the integer k.
 The apparatus of claim 15, wherein the integer k, satisfies k=2^{m}, where m is a positive integer.
 The apparatus of claim 16, wherein the dominant component acquirer determines the first transformation matrix based on one of a Discrete Fourier Transformation (DFT), a Discrete Cosine Transformation (DCT), a Walsh Transformation, and a Hadamard Transformation.
 The apparatus of claim 16, wherein dominant component acquirer determines the first transformation matrix based on a KarhunenLoeve (KL) Transformation used for acquiring dominant components using an autocorrelation characteristic of the first color components.

The apparatus of claim 18, wherein the dominant component acquirer comprises:
an autocorrelation matrix calculator for calculating an autocorrelation matrix of normalized values of elements of the first color component set;
an eigenvector calculator for calculating an eigenvector by KLtransforming the autocorrelation matrix;
a dynamic range compensator for compensating the eigenvector to substantially equalize a dynamic range of the first color component set with a dynamic range of the second color component set; and
a bias compensator for compensating a bias of the first color component set to be equalized with a bias of the second color component set.
 The apparatus of claim 19, wherein the first color space is a color space represented by a RGB color component set, the dynamic range compensator normalizes each of the elements of the eigenvector using a L1 norm, and the transformation function is substantially equal to the following matrix: [0.6460.6880.6661.00.2120.7880.3221.00.678].

A color transformation apparatus transforming a color component set representing a color space to another color component set representing a different color space, the color apparatus comprising:
tangible memory for storing a first color component set and a second color component set to correspond to each other; and
a color transformer for transforming the first color component set read from the tangible memory to the second color component set, wherein the color transformer comprises:
a dominant component acquirer for determining a first transformation matrix used for acquiring dominant components of the first color component set according to a predetermined transformation algorithm;
a dynamic range compensator for compensating the first transformation matrix to substantially equalize a dynamic range of the first color component set with a dynamic range of the second color component set; and
a central processor for calculating the second color component set using the compensated first transformation matrix.
 The apparatus of claim 21, further comprising an inverse transformer for inversetransforming the second color component set to the first color component set using an inverse matrix of the compensated first transformation matrix.
 The apparatus of claim 22, wherein the dominant component acquirer determines the first transformation matrix based on a KarhunenLoeve (KL) Transformation used for acquiring dominant components using an autocorrelation characteristic of the first color components.

The apparatus of claim 23, wherein the dominant component acquirer comprises:
an autocorrelation matrix calculator for calculating an autocorrelation matrix of normalized values of elements of the first color component set;
an eigenvector calculator for calculating an eigenvector by KLtransforming the autocorrelation matrix; and
a bias compensator for compensating a bias of the first color component set to be equalized with a bias of the second color component set.
 The apparatus of claim 24, wherein the first color space is a color space represented by a RGB color component set, a dynamic range compensator normalizes each of the elements of the eigenvector using a L1 norm, and the transformation function is substantially equal to the following matrix: [0.6460.6880.6661.00.2120.7880.3221.00.678].

Samsung Electronics Co. Ltd
(Jan 14 2005)
Explore more patents:

Samsung Electronics Co Ltd
Explore more patents:

Kim Hyunmun
Explore more patents:

Cho Daesung
Explore more patents:

Kim Wooshik
Explore more patents:
CPC Classifications
IPC Classifications

G06K9/00
Explore more patents:

G06T11/00
Explore more patents:

G06T9/00
Explore more patents:

G09G5/02
Explore more patents:

H04N1/46
Explore more patents:

H04N1/60
Explore more patents:

H04N1/64
Explore more patents:

H04N7/26
Explore more patents:

H04N7/30
Explore more patents:

H04N9/64
Explore more patents:
Document Preview
Document History
 Publication: Jun 15, 2010

Application:
Dec 3, 2004
US 243104 A

Priority:
Dec 3, 2004
US 243104 A

Priority:
Mar 8, 2004
KR 20040015605 A

Priority:
Dec 5, 2003
US 52701703 P