US RE41400 E1 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 block is smoothed based on a threshold value and the smoothing value.
Claims(32) 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
3. The method according to
_{VB}(f), M_{HB}(f), M_{VW}(f), M_{HW}(f), M_{T}(f) are defined as;
M _{VB}(f)=∥Q_{VB}f∥^{2}+α_{VB}∥g−f∥_{w1} ^{2} M _{HB}(f)=∥Q_{HB}f∥^{2}+α_{HB}∥g−f∥_{w2} ^{2} M _{VW}(f)=∥Q_{VW}f∥^{2}+α_{VW}∥g−f∥_{w3} ^{2} M _{HW}(f)=∥Q_{HW}f∥^{2}+α_{HW}∥g−f∥_{w4} ^{2} M _{T}(f)=∥Q_{T}f∥^{2}+α_{T}∥g−f∥_{w5} ^{2} 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
5. The method according to
Q ^{2} _{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
7. The method according to
8. The method according to
9. The method according to
_{k+1 }is represented by;
f _{k+1}=f_{k}+β[Ag−Bf_{k}], A=α _{VB}W_{1}+α_{HB}W_{2}+α_{VW}W_{3}+α_{HW}W_{4}+α^{T}W_{5} B=(Q ^{T} _{VB}Q_{VB}+Q^{T} _{HB}Q_{HB}+Q^{T} _{VW}Q_{VW}+Q^{T} _{HW}Q_{HW}+Q^{T} _{T}Q_{T})+A and, β is a relaxation parameter having a convergence property, and computed at the range of
an eigen value λ(A) of the matrix A being replaced by a fixed value.
10. The method according to
11. The method according to
12. The method according to
_{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
14. The method according to
15. The method according to
_{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
19. The method according to
_{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
_{VB}(f), M_{HB}(f), M_{VW}(f), M_{HW}(f), M_{T}(f) are defined as;
M _{VB}(f)=∥Q_{VB}f∥^{2}+α_{VB}∥g−f∥_{w1} ^{2} M _{HB}(f)=∥Q_{HB}f∥^{2}+α_{HB}∥g−f∥_{w2} ^{2} M _{VW}(f)=∥Q_{VW}f∥^{2}+α_{VW}∥g−f∥_{w3} ^{2} M _{HW}(f)=∥Q_{HW}f∥^{2}+α_{HW}∥g−f∥_{w4} ^{2} M _{T}(f)=∥Q_{T}f∥^{2}+α_{T}∥g−f∥_{w5} ^{2} 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
22. The method according to
Q ^{2} _{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
24. The method according to
25. The method according to
_{k+1 }is represented by;
f _{k+1}=f_{k}+β[Ag−Bf_{k}], A=α
_{VB}W_{1}+α_{HB}W_{2}+α_{VW}W_{3}+α_{HW}W_{4}+α_{T}W_{5} B=(Q ^{T} _{VB}Q_{VB}+Q^{T} _{HB}Q_{HB}+Q^{T} _{VW}Q_{VW}+Q^{T} _{HW}Q_{HW}+Q^{T} _{T}Q_{T})+A and, β is a relaxation parameter having a convergence property, and computed at the range of
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 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; obtaining a smoothing value of the current image based on the difference value; and smoothing a pixel value around a boundary of the block based on a threshold value and the smoothing value; and wherein the threshold value is based on quantization information of at least a partially restored portion of the current image. 28. The method of
29. The method of
30. The method of
31. The method of
32. The method of
Description
1. Field of the Invention The present invention relates to an image process 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, and an apparatus therefor. 2. Description of the Background Art In general, image compression techniques, such as MPEG1 and MPEG2 employ a hybrid motion compensation discrete cosine transform (hereinafter, referred to as hybrid MC/DCT) mechanism in order to improve compression efficiency. The hybrid MC/DCT mechanism is roughly divided into an encoding process and a decoding process. In the encoding process, an original image is divided into a plurality of blocks in order to compress information in a spatial section, a second-dimensional discrete cosine transform is performed on each block, and redundancy information in the image or between the images is reduced by using the correlation on a time axis among the images in order to decrease information in a temporal section. In the decoding process, the steps of the encoding process are performed in a reverse order. An encoder and a decoder are necessary to carry out the hybrid MC/DCT mechanism. However, information of the original image signal is lost during the process of coding the image signal described above, especially during the quantization process, thereby causing blocking artifacts and ringing effects to the image which is reconstructed in the decoder. The blocking artifacts imply irregularity between the blocks generated due to information loss resulting from the quantization of the low-frequency DCT coefficients, and the ringing effects result from quantization errors of the high-frequency DCT coefficients. That is, in accordance with a coding technique using the DCT in a coding system of a static image or dynamic image, an image is divided into a plurality of blocks, and the DCT is performed on each block. On the other hand, when the DCT is carried out on the original image, its important information is mainly included in low-frequency elements, and becomes lesser in high-frequency elements. Furthermore, the low-frequency elements include a lot of information relating to adjacent blocks. The DCT does not consider the correlation between the blocks, and quantizes the low-frequency elements by blocks, thereby destroying continuity of the adjacent blocks. It is called the blocking artifacts. In addition, when the coefficients obtained by performing the DCT are quantized, as a quantization interval is increased, the elements to be coded are decreased, and thus the number of the bits to be processed is reduced. As a result, the information of the high-frequency element included in the original image is reduced, thereby generating distortion of the reconstructed image. It is called the ringing effects. The ringing effects generated by increasing the quantization interval are serious especially in a contour of an object in the reconstructed image. As techniques for removing the blocking artifacts and the ringing effects, employed are a low pass filtering method and a regularized image restoration method. According to the low pass filtering method, a plurality of pixels around a predetermined pixel are selected, and an average value thereof is computed. Here, a filter tap or filter coefficients are set by experience. For example, referring to The regularized image restoration method adaptively deals with the blocking artifacts in accordance with statistical properties of the image. That is, irregular information around the boundary of the block or in the block is all computed. However, the computed values form a matrix shape, and thus a real time processing is difficult due to the great computation amount. In addition, an average value obtained by a computation result of the irregular information is equally applied to the pixels, regardless of a degree of irregularity. Accordingly, when a block has a high degree of irregularity, it can be reduced. However, in case of a block having a low degree of irregularity, it may be increased. Thus, the system is not adaptive. Also, the information in the temporal section is not processed, and thus irregularity between the images cannot be adaptively processed. It is therefore an object of the present invention to provide a method for restoring a compressed image of an image processing system and an apparatus therefor which can reduce the blocking artifacts and ringing effects generated in a restored image signal. It is another object of the present invention to provide a method for restoring a compressed image of an image processing system and an apparatus therefor which consider a smoothing degree of an image and reliability for an original image by pixels having an identical property in image block units, during a decoding process. In order to achieve the above-described objects of the present invention, there is provided a method for restoring a compressed image of an image processing system including: a step of defining a smoothing functional having a degree of smoothing 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. These and other objects of the present application will become more readily apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.
The present invention will become better understood with reference to the accompanying drawings which are given only by way of illustration and thus are not limitative of the present invention, wherein: According to the present invention, a smoothing functional is defined in regard to pixels having an identical property by blocks, a regularization parameter is computed based on the smoothing functional, and available values are applied to the regularization parameter, thereby obtaining an image to be restored. Thereafter, an iterative technique, a discrete cosine transform (DCT), a projection and an inverse DCT are sequentially performed on the obtained image, thereby restoring a similar image to the original image. The whole processes will now be described in detail. Definition of Smoothing Functional When an original image (f) is compressed and transmitted, an image (g) reconstructed in the decoder Here, g and f indicate row vectors re-arranged in a stack-order, namely a scanning order, and n indicates a quantization error. When it is presumed that a size of the image is MΧM, the original image (f), the reconstructed image (g) and (n) are column vectors having a size of MΧ1. An original pixel for the original image (f) is represented by f(i,j). Here, i and j indicate a position of the pixel in the image. The 8Χ8 pixels in the block are classified into the pixels having an identical property. That is, the pixels are divided in accordance with their position, vertical direction, horizontal direction and smoothing variation in the temporal section. Accordingly, it is defined that a set of the pixels positioned at a boundary of the block in a vertical direction is C Here, the set C The smoothing functional M(f) for using the regularization restoration method from the above-defined sets C Here, M M Here, first terms in each expression indicate a smoothing degree for the original pixel (reference pixel) and adjacent pixel, and second terms indicate reliability for the original pixel and the restored pixel. ∥.∥ indicates the Euclidean norm. Q The first term at the right side is represented by the following expression.
The smoothing functionals represented by Expression (4) are quadratic equations, respectively. Thus, local minimizers of each smoothing functional become global minimizers. α Thereafter, the regularization parameters, α Approximation of Regularization Parameters Approximation of the regularization parameters is a major element determining performance of the smoothing functional. In order to reduce the computation amount, presumptions are made as follows. - (1) A maximum value of the quantization error generated in the quantization process of the DCT region is Qp, and thus it is presumed that the quantization variables Qp are regular in each macro block. For this, the maximum quantization error of the DCT coefficients of each macro block is regularly set to be Qp.
- (2) It is also presumed that the DCT quantization errors have the Gaussain distribution property in the spatial section.
Under the above presumptions, in case a set theoretic is applied, each regularization parameter is approximated as follows.
Here, Q In Expression (6), denominator terms of the respective regularization parameters are a sum of the energy for the quantization noise of the elements included in each group. As described above, the values of the regularization parameters may be easily computed by applying the set theoretic under the two presumptions. Computing Pixels to be Restored From Smoothing Functional Only the original image needs to be computed. However, the smoothing functional includes a square term of the original image. Accordingly, in order to compute the original image, a gradient operation is carried out on the smoothing functional in regard to the original image. A result value thereof is 0, and represented by the following expression.
Here, a superscript T indicates a transposition of the matrix. A restored image similar to the original image (f) can be obtained by Expression (7). However, operation of an inverse matrix must be performed, and thus the computation amount is increased. Thus, in accordance with the present invention, the restored image is computed by an iterative technique which will now be explained. Iterative Technique When Expression (7) is iterated k times, an iterative solution f In Expression (8), β is a relaxation parameter having a convergence property. Expression (8) can be represented by the following expression by computing consecutive iterative solutions.
Here, I is an identity matrix, and the matrix B has a positive definite property. Therefore, when the following condition is satisfied, the iterative solutions are converged.
Expression (10) can be summarized as follows.
In Expression (11), λ(A) depicts an eigen value of the matrix A. A considerable amount of computation is required to compute the eigen value λ(A). However, the high pass filters have a certain shape determined according to the positions of the respective pixels, regardless of the image. Accordingly, before computing Expression (8), the eigen value λ(A) can be replaced by a fixed value. The value may be computed by a power method which has been generally used in interpretation of numerical values. For example, a computation process of an eigen value of an iterative solution will now be explained.
In the above expression, if k is to infinity, the eigen value λ′ is approximated to a real value. Thus, the iterative solution represented by Expression (8) is computed. The next thing to be considered is a time of finishing the iterative technique, in order to determine the number of iteration. Here, two standards are set as follows. Firstly, a predetermined threshold value is set before starting iteration, an image obtained after iteration, namely a partially-restored image is compared with the previously-set threshold value, and it is determined whether the iteration technique is continuously performed according to a comparison result. Secondly, the iteration technique is performed as many as a predetermined number, and then finished. According to the first standard, a predetermined threshold value is set in performing iteration, and thus a wanted value is obtained. However, although the iteration number is increased, it may happen that the predetermined threshold value is not reached. On the other hand, the second standard is performed by experience, but can reduce a computation amount. Therefore, the two standards may be selectively used according to the design specification. In the step S Here, Qp is a maximum quantization error as explained above, and G(u,v) is a two-dimensional DCT coefficient obtained by performing the DCT on the reconstructed image (g). The DCT coefficients F In the step S Expression (14) will now be described. When F In the step S Here, B indicates the DCT, P indicates mapping, and B The restored image is stored in a frame memory in the post processing unit The post processing unit As discussed earlier, the present invention can restrict a section of the restored image for the respective pixels by using the various regularization parameters. In addition, the present invention prevents flickering which may occur in the dynamic image compression technique. Consequently, the present invention adaptively prevents the blocking artifacts and the ringing effects for the pixels having an identical property in image block units, and thus can be widely used for the products of the hybrid MC-DCT mechanism. As the present invention may be embodied in several forms without departing from the spirit or essential characteristics thereof, it should also be understood that the above-described embodiment is not limited by any of the details of the foregoing description, unless otherwise specified, but rather should be construed broadly within its spirit and scope as defined in the appended claims, and therefore all changes and modifications that fall within the meets and bounds of the claims, or equivalences of such meets and bounds are therefore intended to be embraced by the appended claims. Patent Citations
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