Method of decoding a current image

• US RE41,835 E1
• Filed: 08/20/2007
• Issued: 10/19/2010
• Est. Priority Date: 11/13/1998
• Status: Expired due to Term

• Current Assignee: LG Electronics, Inc. (LG Corporation)
• Original Assignee: LG Electronics, Inc. (LG Corporation)
• Inventors: Hong, Min-Cheol
• Primary Examiner(s): Worku; Negussie
• Application Number:
  US11/892,178
• Time in Patent Office: 1,156 Days
• Field of Search: 358/474, 358/539, 358/426.07, 358/261.3, 358/426.03, 358/426.13, 382/232, 382/233, 382/268, 375/240.16, 375/240.22, 375/E7.19, 375/E7.134, 375/E7.177, 375/240, 348/475, 348/700
• US Class Current: 358/474
• CPC Class Codes: H04N 19/192 the adaptation method, adaptation tool or adaptation type being iterative or recursive
• CPC Class Codes: H04N 19/86 involving reduction of coding artifacts, e.g. of blockiness

Abstract

The present invention relates to an image processing technique, and in particular to a method for restoring a compressed image by using a hybrid motion compensation discrete cosine transform (hybrid MC/DCT) mechanism, including: a step of defining a smoothing functional having a smoothing degree of an image and reliability for an original image by pixels having an identical property in image block units; and a step of computing a restored image by performing a gradient operation on the smoothing functional in regard to the original image, thereby preventing the blocking artifacts and the ringing effects in regard to the pixels having an identical property in image blocks.In one embodiment, the method includes obtaining a pixel value in a current block and at least one adjacent pixel value, obtaining a difference value between the pixel value in the current block and the adjacent pixel value, and obtaining a smoothing value of the current image based on the difference value. A pixel value around a boundary of the current block is smoothed based on the smoothing value and quantization information for the current block.

Claims

1. A method for restoring a compressed image of an image processing system, comprising:

a step for defining a smoothing functional having a smoothing degree of an image and reliability for an original image by pixels having an identical property in image block units; and

a step for computing a restored image by performing a gradient operation on the smoothing functional in regard to the original image;

wherein the smoothing functional M(f) comprises a sum of a smoothing functional M_VB(f) for pixels positioned at the boundary of a block in a vertical direction, a smoothing functional M_VW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_HB(f) for pixels positioned at the boundary of a block in a horizontal direction, a smoothing functional M_HW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_T(f) for pixels moved and compensated in the temporal section, “

f”

indicating the original image.

2. The method according to claim 1, wherein the step for defining the smoothing functional divides the pixels according to their position, horizontal direction, vertical direction and smoothing variation in a temporal section.

3. The method according to claim 1, wherein the smoothing functionals M_VB(f), M_HB(f), M_VW(f), M_HW(f), M_T(f) are defined as;

M_VB(f)=∥

Q_VBf∥

²+α

_VB∥

g−

f∥

²_W1

M_HB(f)=∥

Q_HBf∥

²+α

_HB∥

g−

f∥

²_W2

M_VW(f)=∥

Q_VWf∥

²+α

_VW∥

g−

f∥

²_W3

M_HW(f)=∥

Q_HWf∥

²+α

_HW∥

g−

f∥

²_W4

M_T(f)=∥

Q_Tf∥

²+α

_T∥

g−

f∥

²_W5

Q_VB, Q_VW, Q_HB, Q_HW, Q_T indicating high pass filters for smoothing the respective pixels, α

_VB, α

_VW, α

_HB, α

_HW, α

_T being regularization parameters, g being a reconstructed image, and W1, W2, W3, W4, W5 indicating diagonal matrixes for determining whether each group has an element.

4. The method according to claim 1, wherein the step for computing the restored image comprises a step for approximating the regularization parameter by applying a set theoretic, and it is presumed that the quantization variables of the DCT region regular in each macro block, and also presumed that the DCT quantization errors have the Gaussain distribution property in the spatial section.

5. The method according to claim 4, wherein the regularization parameters are approximated as;

${\mathrm{\alpha <br><br>}}_{\mathrm{VB}}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>1}^2}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>1}^2}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_1<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{HB}}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>2}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>2}^2}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_2<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{VW}}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>3}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>3}^2}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_3<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{HW}}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>4}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>4}^2}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_4<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_T=\frac{{<br><br>Q_T<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>5}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_T<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>5}^2}=\frac{{<br><br>Q_T<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_5<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$

Q²_P(m,n) indicating a quantization variable of a macro block including an (m,n)th pixel of a two-dimensional image.

6. The method according to claim 1, wherein a local minimizer of the smoothing functional is a global minimizer.

7. The method according to claim 1, wherein the regularization parameter indicates a ratio of a smoothing degree of the image and reliability for the original image.

8. The method according to claim 1, further comprising a step for computing an iterative solution in regard to a restored image, after computing the restored image.

9. The method according to claim 8, wherein the iterative solution f_k+1 is represented by;

f_k+1=f_k+β

[Ag−

Bf_k],

A=α

_VBW₁+α

_HBW₂+α

_VWW₃+α

_HWW₄+α

_TW₅

B=(Q^T_VBQ_VB+Q^T_HBQ_HB+Q^T_VWQ_VW+Q^T_HWQ_HW+Q^T_TQ_T)+A

and, β

is a relaxation parameter having a convergence property, and computed at the range of $0<<br><br>\mathrm{\beta <br><br>}<<br><br>\frac{2}{1+{\max }_i<br><br>{\mathrm{\lambda <br><br>}}_i<br><br>\left(A\right)},$

 

an eigen value λ

(A) of the matrix A being replaced by a fixed value.

10. The method according to claim 8, wherein a predetermined threshold value is set in computing an iterative solution, an image obtained after iteration is compared with the previously-set threshold value, and it is determined whether the iteration technique is continuously performed according to a comparison result, or the iteration is finished after the iteration technique is performed as many as a previously-set number.

11. The method according to claim 8, further comprising a step for obtaining a mapped image by projecting a two-dimensional DCT coefficient of the restored image corresponding to a computed iterative solution, and for performing an inverse DCT on the mapped image.

12. The method according to claim 11, wherein the step for obtaining the mapped image is mapping a projected restored image P(F_k+1(u,v)) to G(u,v)−

Qp when the DCT coefficient of the restored image F_k+1(u,v) is smaller than G(u,v)−

Qp, mapping the projected restored image P(F_k+1(u,v)) to G(u,v)+Qp when F_k+1(u,v) is greater than G(u,v)+Qp, and otherwise mapping the projected restored image P(F_k+1(u,v)) as it is, G(u,v) indicating a two-dimensional DCT coefficient obtained by performing the DCT on the reconstructed image, and Qp indicating quantization information.

13. The method according to claim 1, wherein a predetermined threshold value is set in computing an iterative solution, an image obtained after iteration is compared with the previously-set threshold value, and it is determined whether the iteration technique is continuously performed according to a comparison result, or the iteration is finished after the iteration technique is performed as many as a previously-set number.

14. The method according to claim 1, further comprising a step for obtaining a mapped image by projecting a two-dimensional DCT coefficient of the restored image corresponding to a computed iterative solution, and for performing an inverse DCT on the mapped image.

15. The method according to claim 14, wherein the step for obtaining the mapped image is mapping a projected restored image P(F_k+1(u,v)) to G(u,v)−

Qp when the DCT coefficient of the restored image F_k+1(u,v) is smaller than G(u,v)−

Qp, mapping the projected restored image P(F_k+1(u,v)) to G(u,v)+Qp when F_k+1(u,v) is greater than G(u,v)+Qp, and otherwise mapping the projected restored image P(F_k+1(u,v)) as it is, G(u,v) indicating a two-dimensional DCT coefficient obtained by performing the DCT on the reconstructed image, and Qp indicating quantization information.

16. An apparatus for restoring a compressed image of an image processing system, comprising:

a decoder for decoding a coded image signal, and for outputting information of the restored image, such as the decoded image, a quantization variable, a macro block type and a motion vector; and

a post processing unit for including the information of the restored image inputted from the image decoder, for defining a smoothing functional including a sum of a smoothing functional M_VB(f) for pixels positioned at the boundary of a block in a vertical direction, a smoothing functional M_VW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_HB(f) for pixels positioned at the boundary of a block in a horizontal direction, a smoothing functional M_HW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_T(f) for pixels moved and compensated in the temporal section, “

f”

indicating the original image, and for performing a gradient operation on the smoothing functional in regard to the original image,

the smoothing functional including a regularization parameter having weight of reliability for the original image.

17. A method for restoring a compressed image of an image processing system, comprising:

a step for defining a smoothing functional having a smoothing degree of an image and reliability for an original image by pixels having an identical property in image block units;

a step for computing a restored image by performing a gradient operation on the smoothing functional in regard to the original image; and

a step for computing an iterative solution in regard to the restored image, after computing the restored image.

18. The method according to claim 17, wherein the step for defining the smoothing functional divided the pixels according to their position, horizontal direction, vertical direction and smoothing variation in a temporal section.

19. The method according to claim 17, wherein the smoothing functional M(f) comprises a sum of a smoothing functional M_VB(f) for pixels positioned at the boundary of a block in a vertical direction, a smoothing functional M_VW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_HB(f) for pixels positioned at the boundary of a block in a horizontal direction, a smoothing functional M_HW(f) for pixels positioned inside the block in a horizontal direction, a smoothing functional M_T(f) for pixels moved and compensated in the temporal section, “

f”

indicating the original image.

20. The method according to claim 19, wherein the smoothing functionals M_VB(f), M_HB(f), M_VW(f), M_HW(f), M_T(f) are defined as;

M_VB(f)=∥

Q_VBf∥

²+α

_VB∥

g−

f∥

²_W1

M_HB(f)=∥

Q_HBf∥

²+α

_HB∥

g−

f∥

²_W2

M_VW(f)=∥

Q_VWf∥

²+α

_VW∥

g−

f∥

²_W3

M_HW(f)=∥

Q_HWf∥

²+α

_HW∥

g−

f∥

²_W4

M_T(f)=∥

Q_Tf∥

²+α

_T∥

g−

f∥

²_W5

Q_VB, Q_VW, Q_HB, Q_HW, Q_T indicating high pass filters for smoothing the respective pixels, α

_VB, α

_VW, α

_HB, α

_HW, α

_T being regularization parameters, g being a reconstructed image, and W1, W2, W3, W4, W5 indicating diagonal matrixes for determining whether each group has an element.

21. The method according to claim 17, wherein the step for computing the restored image comprises a step for approximating the regularization parameter by applying a set theoretic, and it is presumed that the quantization variables of the DCT region are regular in each macro block, and also presumed that the DCT quantization errors have the Gaussain distribution property in the spatial section.

22. The method according to claim 21, wherein the regularization parameters are approximated as;

${\mathrm{\alpha <br><br>}}_{\mathrm{VB}}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>1}^2}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>1}^2}=\frac{{<br><br>Q_{\mathrm{VB}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_1<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{HB}}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>2}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>2}^2}=\frac{{<br><br>Q_{\mathrm{HB}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_2<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{VW}}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>3}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>3}^2}=\frac{{<br><br>Q_{\mathrm{VW}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_3<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_{\mathrm{HW}}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>4}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>4}^2}=\frac{{<br><br>Q_{\mathrm{HW}}<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_4<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$${\mathrm{\alpha <br><br>}}_T=\frac{{<br><br>Q_T<br><br>f<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>5}^2}<br><br>\text{\hspace{1em}<br><br>}=\frac{{<br><br>Q_T<br><br>g<br><br>}^2}{{<br><br>g-f<br><br>}_{W<br><br>5}^2}=\frac{{<br><br>Q_T<br><br>g<br><br>}^2}{\underset{n}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{m}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>w_5<br><br>\left(m,n\right)<br><br>{\mathrm{Qp}}^2<br><br>\left(m,n\right)}$

Q²_P(m,n) indicating a quantization variable of a macro block including an (m,n)th pixel of a two-dimensional image.

23. The method according to claim 17, wherein a local minimizer of the smoothing functional is a global minimizer.

24. The method according to claim 17, wherein the regularization parameter indicates a ratio of a smoothing degree of the image and reliability for the original image.

25. The method according to claim 17, wherein the iterative solution f_k+1 is represented by;

f_k+1=f_k+β

[Ag−

Bf_k],

A=α

_VBW₁+α

_HBW₂+α

_VWW₃+α

_HWW₄+α

_TW₅

B=(Q^T_VBQ_VB+Q^T_HBQ_HB+Q^T_VWQ_VW+Q^T_HWQ_HW+Q^T_TQ_T)+A

and, β

is a relaxation parameter having a convergence property, and computed at the range of $0<<br><br>\mathrm{\beta <br><br>}<<br><br>\frac{2}{1+{\max }_i<br><br>{\mathrm{\lambda <br><br>}}_i<br><br>\left(A\right)},$

 

an eigen value λ

(A) of the matrix A being replaced by a fixed value.

26. An apparatus for restoring a compressed image of an image processing system, comprising:

a decoder for decoding a coded image signal, and for outputting information of the restored image, such as the decoded image, a quantization variable, a macro block type and a motion vector; and

a post processing unit for including the information of the restored image inputted from the image decoder, for defining a smoothing functional including a smoothing degree of the image and reliability of an original image block unit, and for performing a gradient operation on the smoothing functional in regard to the original image,

the smoothing functional including a regularization parameter having weight of reliability for the original image.

27. A method of decoding a current image, comprising:

obtaining, at a decoding apparatus, a pixel value in a current block and at least one adjacent pixel value;

obtaining, at the decoding apparatus, a difference value between the pixel value in the current block and the adjacent pixel value;

obtaining, at the decoding apparatus, a smoothing value of the current image based on the difference value;

smoothing, at the decoding apparatus, a pixel value around a boundary of the current block based on the smoothing value and quantization information for the current block, wherein

the quantization information includes a quantization parameter corresponding to discrete cosine transform (DCT) of the current block.

28. The method of claim 27, wherein the adjacent pixel value is obtained from a block different than the current block.

29. The method of claim 27, wherein the smoothed pixel value is the obtained pixel value in the current block.

30. The method of claim 27, wherein the adjacent pixel value is adjacent to the obtained pixel value in the current block in a vertical direction.

31. The method of claim 27, wherein the adjacent pixel value is adjacent to the obtained pixel value in the current block in a horizontal direction.