Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6-7, 11-12, 14, and 16-17 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Klassen (U.S. Patent Publication No. 2009/0257072).
Regarding claim 1, Klassen discloses a method for compressing data representative of a mapping for use in image processing, the data comprising a plurality of mappings representing a look-up table, said mappings being from a respective set of one or more input pixel attribute values representing respectively one or more input data channels, to an associated set of one or more output pixel attribute values representing respectively one or more output data channels, the method comprising [Klassen: 0035 “The presently described embodiments relate to a technique to compute an inexpensive fit (e.g. inexpensive to store and to evaluate) to the contents of a multidimensional lookup table”][Klassen: 0036 “the buffer memory 33 is configured to house multidimensional color look-up tables in which the input print data is organized”]: determining, based on the plurality of mappings, one or more parameters of a function for transforming a given set of one or more input pixel attribute values representing respectively the input data channels, into a set of one or more estimated output pixel attribute values representing respectively the output data channels [Klassen: 0039 “The method 200, in one form, is initiated upon the reception of input color data (at 202). It should be appreciated that this input data is stored in the multidimensional color look-up tables. These tables may reside in any suitable location on the system, including within the buffer memory 33. Next, a mathematical model is fit to the input data (at 204). In one form, the mathematical model is a tensor product B-spline model”](teaches parameters of the mathematical model); for a plurality of the sets of input pixel attribute values [Klassen: 0042 “It should be understood that the mathematical model is evaluated to obtain a result for each node in the table. So, the routine selects a node and initializes its location by setting n=1 (at 206). The evaluation, as described above (for example), is then conducted on the data for the first node (at 208) and a result is obtained”](teaches evaluating the model at LUT nodes and each node represents a set of input color channel values): determining, based on the function and the set of input pixel attribute values, a set of approximate values of one or more of the associated set of output pixel attribute values [Klassen: 0040 “Thus, to find a point at coordinates (X, Y. Z), the control vertices in the X dimension are treated as control vertices of one-dimensional curves, and all the curves are evaluated at x=X. In the 5x5x5 case, there are 25 such one dimensional curves defined, and 25 values result from the initial evaluations, all lying in the XX plane. These values are then treated as control vertices offive one dimensional curves running in they dimension, and those curves are evaluated at y=Y, yielding five control vertices. Finally, those five values are treated as control vertices of a single curve varying in the Z direction; it is evaluated at Z=Z to yield the final point”](teaches how the fitted B-Spline model is evaluated at a coordinate/input point, XYZ correspond to input channel values); and determining, based on the associated set of output pixel attribute values and the set of approximate values, a set of one or more residual output pixel attribute values [Klassen: 0043 “A difference between the result for the node (or the rounded result) and the input data at the node is then com puted (at 212). This process may be conducted in a variety of Suitable manners which are a function of the mathematical model that is used.”](teaches computing the difference between the model result and the original LUT value at the node); and storing data representative of the parameters of the function and data representative of the sets of residual output pixel attribute values [Klassen: 0044 “With reference back to FIG. 2, the difference between the result for the node (or the rounded result) and the input data at the node is then optionally stored (at 214). In one form, the difference values are typically small, e.g. Small enough to fit within a byte of data. As an example, the differences could be 0, +1, or -1”](teaches storing the residual/difference values).
Regarding claim 2, Klassen discloses the method of claim 1, comprising compressing said residual output pixel attribute values to obtain compressed residual output pixel attribute values [Klassen: 0045 “If all nodes of the input data (or multidimensional color look-up table) have been evaluated, then all the differences (in some embodiments, these may be stored differences) are compressed. Any Suitable compression technique may be used”], and wherein the data representative of the sets of residual output pixel attribute values is data representative of the compressed residual output pixel attribute values (Klassen: Abstract “In one form, the parameters of the fit and the differences are stored and compressed, possibly losslessly”)(teaches storing and compressing the differences).
Regarding claim 3, Klassen discloses the method of claim 1, wherein determining the set of residual output pixel attribute values comprises performing a difference operation with the associated set of predetermined output pixel attribute values and the set of approximate values [Klassen: 0042 “It should be understood that the mathematical model is evaluated to obtain a result for each node in the table. So, the routine selects a node and initializes its location by setting n=1 (at 206). The evaluation, as described above (for example), is then conducted on the data for the first node (at 208) and a result is obtained. In some embodiments, the result is rounded (at 210)”][Klassen: 0043 “A difference between the result for the node (or the rounded result) and the input data at the node is then computed (at 212). This process may be conducted in a variety of Suitable manners which are a function of the mathematical model that is used.”](teaches first evaluating the mathematical model at a LUT node to obtain a model generated result which corresponds to set of approximate values).
Regarding claim 6, Klassen discloses a method for determining a mapping from a set of one or more input pixel attribute values representing respectively one or more input data channels, to a set of one or more output pixel attribute values representing respectively one or more output data channels, the method comprising [Klassen: 0049 “a method 400 for decompressing the color look-up table data is shown”]: obtaining one or more parameters of a function for transforming a given set of one or more input pixel attribute values representing respectively the input data channels, into a set of one or more output pixel attribute values representing respectively the output data channels (Klassen: Abstract “the parameters of the fit and the differences are stored and compressed”)(teaches storing parameters of the fitted mathematical model during compression, and the decompression method uses that mathematical model); obtaining a set of one or more residual output pixel attribute values representing respectively the output data channels [Klassen: 0049 “The differences (which were previously determined and may be stored) are then decompressed (at 404)”](teaches the differences which are the residual values previously computed between the model result and original LUT data, during decompression, Klassen obtains those residuals by decompressing the stored differences); determining, based on the function and the set of input pixel attribute values, a set of one or more approximate output pixel attribute values representing respectively the output data channels [Klassen: 0049 “the mathematical model is evaluated at each node to obtain an evaluated”]; and determining, based on the set of approximate values and the set of residual output pixel attribute values, the set of output pixel attribute values representing respectively the output data channels [Klassen: 0049 “the evaluated or second results are added to the decompressed differences”].
Regarding claim 7, Klassen discloses the method of claim 6, wherein determining the set of output pixel attribute values comprises performing an addition operation with the set of approximate output pixel attribute values and the set of residual output pixel attribute values [Klassen: 0018 “comprises evaluating the mathematical model at the nodes, decompressing the differences, and, adding the differences to the evaluated values”].
Regarding claim 11, Klassen discloses the method of claim 1, wherein the function comprises an affine transformation (interpreted as a transformation of the form output = matrix x input + offset/bias) [Klassen: 0041 “It should be appreciated that, in addition to the noted tensor product B-spline model, a variety of suitable mathematical models capable of fitting and evaluation at selected data nodes will Suffice. For example, a polynomial matrix approach may be used, as described in Hardeberg's thesis, J. Hardeberg, Acquisition and Reproduction of Colour Images Colorimetric and Multispectral Approaches, Doctoral Dissertation, I'Ecole Nationale Supérieure des Télécommunications (Paris 1999) which is incorporated herein by reference. In this method, as a color is converted, its components are raised to various powers and multiplied by each other, to form a vector of coefficients, which are then multiplied by a suit able matrix, yielding a resulting vector which is the converted colour.”](teaches that the fitted function/model may be a polynomial matrix transformation, this is a function that transforms input color components into output color components by forming a coefficient vector and multiplying by a matrix).
Regarding claim 12, Klassen discloses the method of claim 1, wherein the input data channels comprise three colour channels, and the output data channels comprise the three colour channels [Klassen: 0047 “a minimal quality lookup table map ping from CMYK to L*a*b*, may be sampled at 17x17x17 with 8 bit values per output channel, at each node. This is sufficient for a single, fixed UCR/GCR (Under Color Removal/Gray Component Replacement) mapping, so that for any CMY combination there is only one CMYK that would be printed. Hence, it can be stored as a 3 dimensional table”](teaches 3 channel color data which have 8 bit values per output channel).
Regarding claim 14, Klassen discloses the method of claim 1, wherein the input data channels comprise a channel for a first pixel characteristic and a channel for a second pixel characteristic, wherein the second pixel characteristic is different from the first pixel characteristic [Klassen: 0036 “FIG. 1 is a schematic block diagram of an embodiment of a printing apparatus into which the presently described embodiments may be implemented. The system includes an interface 31 that receives print data, for example, from a host computer, and stores the print data in a buffer memory 33. In at least one form, the buffer memory 33 is configured to house multidimensional color look-up tables in which the input print data is organized. In at least one form, as a result of the implementation of the presently described embodiments, compressed color look-up tables are stored in the buffer memory 33. Moreover, the input color data may include CMY color space data or L*a*b* color space data.”][Klassen: 0047 “a minimal quality lookup table map ping from CMYK to L*a*b*, may be sampled at 17x17x17 with 8 bit values per output channel, at each node. This is sufficient for a single, fixed UCR/GCR (Under Color Removal/Gray Component Replacement) mapping, so that for any CMY combination there is only one CMYK that would be printed. Hence, it can be stored as a 3 dimensional table”].
Claim 16 is a system claim corresponding to claim 12 without any additional limitations. Thus, claim 16 is rejected for the same reasons as claim 12 above.
Claim 17 is a system claim corresponding to claim 1 without any additional limitations. Thus, claim 17 is rejected for the same reasons as claim 1 above.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Klassen (U.S. Patent Publication No. 2009/0257072), in view of Finlayson et al. (U.S. Patent Publication No. 2022/0414848).
Regarding claim 4, Klassen discloses the method of claim 1, but fails to explicitly disclose wherein determining the parameters of the function comprises estimating the function from the plurality of mappings using random sample consensus.
However, Finlayson discloses wherein determining the parameters of the function comprises estimating the function from the plurality of mappings using random sample consensus [Finlayson: 0019 “D and M may be iteratively solved using an alternating least - squares , ALS , procedure . D and M may be iteratively solved using a random sample consensus , RANSAC , procedure . D and M may be iteratively solved using a robust solving method . D and M may be solved using a simple search procedure such as the Nelder - Mead Simplex Method”](teaches determining parameters of a mathematical model/function from LUT data, but uses least squares. Further teaches solving image processing color correction model parameters using RANSAC).
Klassen and Finlayson are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Finlayson’s teachings of using random sample consensus to solve model parameters. The motivation for such a combination would provide the benefit of more robust parameter estimation.
Claims 5 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Klassen (U.S. Patent Publication No. 2009/0257072), in view of Bordes et al. (WO 2014/166705).
Regarding claim 5, Klassen discloses the method of claim 1, but fails to explicitly disclose wherein a total number of the sets of residual output pixel attribute values is fewer than a total number of the mappings in the plurality of mappings.
However, Bordes discloses wherein a total number of the sets of residual output pixel attribute values is fewer than a total number of the mappings in the plurality of mappings (Bordes: Page 11, Lines 10-15 “According to a variant, not all the vertices of the LUT are encoded in the bitstream. For example, if the absolute values of all the residues or of all the quantized residues of a vertex are below a threshold value TH then no residue is encoded for that vertex, e.g. TH=0 or TH=1. A binary flag is thus encoded in the bitstream for each vertex indicating whether or not at least one residue is 15 encoded for that vertex”).
Klassen and Bordes are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Bordes’s teachings of not encoding residuals for every LUT vertex. The motivation for such a combination would provide the benefit of reducing the amount of residual data that must be stored.
Regarding claim 8, Klassen discloses the method of claim 6, but fails to explicitly disclose wherein the set of input pixel attribute values is a target set of input pixel attribute values, and obtaining the set of residual output pixel attribute values comprises: obtaining a plurality of sets of one or more residual output pixel attribute values representing respectively the output data channels, the plurality of sets of residual output pixel attribute values being associated with a respective plurality of sets of one or more input pixel attribute values representing respectively the input data channels; and based on the target set of input pixel attribute values, interpolating from the plurality of sets of one or more residual output pixel attribute values to obtain the set of residual output pixel attribute values.
However, Bordes discloses wherein the set of input pixel attribute values is a target set of input pixel attribute values, and obtaining the set of residual output pixel attribute values comprises (Bordes: Page 9, Lines 9-11 “In a step 50, each of the three color values (Vr, V9, Vb) associated with the 10 current vertex V of coordinates (r, g, b) is predicted from reconstructed color values associated with neighboring vertices”): obtaining a plurality of sets of one or more residual output pixel attribute values representing respectively the output data channels, the plurality of sets of residual output pixel attribute values being associated with a respective plurality of sets of one or more input pixel attribute values representing respectively the input data channels (Bordes: Page 10, Lines 12-15 “In a step 52, three residues are computed for the current vertex, one for each color components: resr=(Vr-½-), res9=(V9-'Vg) and resb=(Vb-Vb)-The residues are then entropy coded in a bitstream or quantized before being entropy coded”)(teaches decompressed differences are residuals, further reaches residues associated with LUT vertices and output color components. Multiple vertices therefore provide a plurality of residual sets associated with respective input coordinate sets); and based on the target set of input pixel attribute values, interpolating from the plurality of sets of one or more residual output pixel attribute values to obtain the set of residual output pixel attribute values (Bordes: Page 16, Lines 17-22 “A prediction is thus determined for each color value using for example a trilinear interpolation as illustrated by Figure 8: l1r = K X Li=o,1Lj=o,1Lk=o,1si(r) x sj(g) x sk(b) x LUT[ri][gj][bk].r where: (ri, gj, bk) with i=0, 1, j=0, 1 and k=0, 1 are the coordinates of the vertices of the parent octant in the 3D color space; (r, g, b) are the coordinates of the current vertex”)(teaches using interpolation based on the current vertex coordinates, applying it to Klassen’s residual/difference data, this teaches obtaining a target residual by interpolating from neighboring residual values).
Klassen and Bordes are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Bordes teachings of obtaining LUT values using interpolation from neighboring residual/reconstructed values. The motivation for such a combination would provide the benefit of determining corrected output values for target inputs that fall between stored LUT nodes.
Regarding claim 9, Klassen discloses the method of claim 8, but fails to explicitly disclose wherein the input data channels comprise three data channels, the plurality of sets of residual output pixel attribute values comprises eight said sets of residual output pixel attribute values, and the interpolating comprises trilinear interpolation from the eight sets of residual output pixel attribute values.
However, Bordes discloses wherein the input data channels comprise three data channels (Bordes: Page 9, Lines 9-13 “In a step 50, each of the three color values (Vr, V9, Vb) associated with the current vertex V of coordinates (r, g, b) is predicted from reconstructed color values associated with neighboring vertices, i.e. vertices which belong to a parent octant of the current octant. (r, g, b) is used instead of (c1, c2, c3) for simplifying the notations”)(teaches 3 channel color input spaces), the plurality of sets of residual output pixel attribute values comprises eight said sets of residual output pixel attribute values (Bordes: Page 6, Line 27 “Figure 14 depicts the position of the 8 vertices of an octant”), and the interpolating comprises trilinear interpolation from the eight sets of residual output pixel attribute values (Bordes: Page 9, Lines 16-20 “A prediction is thus determined for each color value using for example a trilinear interpolation as illustrated by Figure 8: l1r = K X Li=o,1Lj=o,1Lk=o,1si(r) x sj(g) x sk(b) x LUT[ri][gj][bk].r where: (ri, gj, bk) with i=0, 1, j=0, 1 and k=0, 1 are the coordinates of the vertices of the parent octant in the 3D color space”)(teaches trilinear interpolation in a 3D LUT from vertices indexed by i).
Klassen and Bordes are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Bordes teachings of trilinear interpolation. The motivation for such a combination would provide the benefit of applying residual correction in a standard three channel LUT structure.
Regarding claim 10, Klassen discloses the method of claim 6, comprising: obtaining a further set of one or more residual output pixel attribute values representing respectively the output data channels [Klassen: 0049 “The differences (which were previously determined and may be stored) are then decom pressed (at 404)”]; determining, based on the function and a further set of one or more input pixel attribute values representing respectively the input data channels, a further set of one or more approximate output pixel attribute values representing respectively the output data channels [Klassen: 0049 “the mathematical model is evaluated at each node to obtain an evaluated, or second, result (at 402)”]; determining, based on the further set of approximate output pixel attribute values and the further set of residual output pixel attribute values, a further set of one or more output pixel attribute values representing respectively the output data channels [Klassen: 0049 “Then, the evaluated or second results are added to the decompressed differences (at 406). This provides the system with uncompressed input data that can be used to render or print images”]; but fail to explicitly disclose and based on a target set of one or more input pixel attribute values representing respectively the input data channels, interpolating from the set of output pixel attribute values and the further set of output pixel attribute values to obtain a target set of one or more output pixel attribute values representing respectively the output data channels.
However, Bordes discloses and based on a target set of one or more input pixel attribute values representing respectively the input data channels, interpolating from the set of output pixel attribute values and the further set of output pixel attribute values to obtain a target set of one or more output pixel attribute values representing respectively the output data channels (Bordes: page 3, Lines 3-6 “According to a specific characteristic of the invention, predicting the at least one value associated with the current vertex from reconstructed values associated with neighboring vertices comprises interpolating the at least one value from corresponding reconstructed values of the neighboring vertices”).
Klassen and Bordes are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Bordes teachings of interpolating from reconstructed neighboring output values. The motivation for such a combination would provide the benefit of obtaining a target output value from reconstructed LUT values for arbitrary target inputs.
Claims 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Klassen (U.S. Patent Publication No. 2009/0257072), in view of Narasimha et al. (U.S. Patent Publication No. 2014/0152686).
Regarding claim 13, Klassen discloses the method of claim 1, but fails to explicitly disclose wherein the one or more input data channels comprises an input pixel intensity value channel, and the one or more output data channels comprises a tonemapped pixel intensity value channel.
However, Narasimha discloses wherein the one or more input data channels comprises an input pixel intensity value channel, and the one or more output data channels comprises a tonemapped pixel intensity value channel [Narasimha: 0054 “FIG. 6 is a flow diagram of a method for tone map ping of an HDR image that may be performed by the tone mapping component 212 of FIG. 2 or the tone mapping com ponent 316 of FIG. 3. In general, the method takes the 16-bit linear data of the HDR image and adaptively maps the data into a smaller number of bits based on the scene content in the image”](teaches compressing image/color LUT mappings with fitted functions and residuals, further teaches tone mapping a luminance pixel value L to a tone mapped output value).
Klassen and Narasimha are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Narasimha’s teachings of mapping input pixel intensity values to tone mapped pixel intensity values. The motivation for such a combination would provide the benefit of compressing tone mapping data while preserving the tone mapping transformation.
Regarding claim 15, Klassen discloses the method of claim 14, but fails to explicitly disclose wherein the channel for the first pixel characteristic is an input pixel intensity value channel, the channel for the second pixel characteristic is a pixel location channel, and the one or more output data channels comprises a tonemapped pixel intensity value channel.
However, Narasimha discloses wherein the channel for the first pixel characteristic is an input pixel intensity value channel, the channel for the second pixel characteristic is a pixel location channel, and the one or more output data channels comprises a tonemapped pixel intensity value channel [Narasimha: 0061 “where L is computed as a weighted sum of applying the tone curves of the four blocks having center points closest to X(x,y) to L”][Narasimha: 0064 “where CYY is a distance weight based on the distance from the NN neighboring centerpoint to the pixel X(x,y)ando, is an intensity weight based on the difference in intensity between the mean pixel value of the block containing the NN neighboring center point and L”](teaches the compressible LUT framework and notes spatially varying tables, further teaches tone mapping where output L_out depends on both the input luminance/intensity value L and pixel location X(x,y), including distance from neighboring block centers).
Klassen and Narasimha are considered to be analogous to the claimed invention because they are in the same field of image processing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Klassen to incorporate Narasimha’s teachings of using pixel intensity and pixel location to determine a tone mapped pixel intensity value. The motivation for such a combination would provide the benefit of supporting local tone mapping while reducing storage requirements for the mapping data.
Conclusion
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/AHMED TAHA/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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