DETAILED ACTION
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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 06/21/2023. It is noted, however, that applicant has not filed a certified copy of the CN202310751931.4 application as required by 37 CFR 1.55.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by XIE (US 20250330655).
As to claim 1, XIE discloses a neural network-based picture filtering method (FIG. 3), the method comprising:
determining a first data processing mode for to-be-filtered information (FIG. 3, step 302: Determine a geometric transformation type of the current coding tree unit according to the related syntax element; see [0064], it is determined whether a current image block in which the current coding tree unit is located uses a neural network based in-loop filtering technology with performing the geometric transformation on an input … or it is determined to apply another filtering technology), the to-be-filtered information comprising a reconstructed picture block (see [0079], [0081]);
when the first data processing mode is one of a plurality of data rearrangement modes (see [0090], the geometric transformation type includes one of the following: diagonal flip, horizontal flip, vertical flip, or rotation by a preset angle), performing data rearrangement on the reconstructed picture block according to the first data processing mode, to obtain rearranged to-be-filtered information (FIG. 3, step 304; see [0064], If it is determined, according to the first syntax element, to use the neural network based in-loop filtering technology with performing the geometric transformation on an input, the geometric transformation type is further determined; see [0089], Perform geometric transformation on the reference sample information of the current coding tree unit according to the geometric transformation type, to obtain geometric transformed reference sample information);
filtering the rearranged to-be-filtered information by using a neural network filter, to obtain a filtered picture block of the rearranged to-be-filtered information (FIG. 3, step 305; see [0103], Input the geometric transformed reference sample information of the current coding tree unit to a neural network based in-loop filter model for filtering, to output filtered reconstructed sample information); and
performing data inverse rearrangement on the filtered picture block of the rearranged to-be-filtered information according to the first data processing mode, to obtain a filtered reconstructed picture block (FIG. 3, step 306; see [0121], Perform inverse geometric transformation on the filtered reconstructed sample information according to the geometric transformation type, to obtain final reconstructed sample information of a current coding tree unit).
As to claim 2, XIE further discloses further comprising:
when the first data processing mode is a data non-rearrangement mode, directly filtering the to-be-filtered information by using the neural network filter, to obtain the filtered reconstructed picture block (see [0064], if it is determined not to use the neural network based in-loop filtering technology with performing the geometric transformation on an input, it is determined that the input is directed inputted to a neural network based in-loop filter model without being processed by the geometric transformation).
As to claim 3, XIE further discloses wherein the first data processing mode is a data processing mode with a minimum filtering cost in the plurality of data rearrangement modes (see [0203]-[0204]).
As to claim 4, XIE further discloses wherein the determining the first data processing mode for to-be-filtered information comprises:
decoding a bitstream to obtain a first flag indicating whether to perform data rearrangement on the to-be-filtered information during filtering (see [0125]-[0127], sps_nnlf_geotransform_enable_flag … slice_nnlf_geotransform_flag; [0136], If slice_nnlf_geotransform_flag is true, geometric transformation needs to be performed on the non-constant input of the model); and
determining the data non-rearrangement mode as the first data processing mode when the first flag indicates not to perform data rearrangement on the to-be-filtered information during filtering (see [0064]; see [0193], the new syntax element sps_nnlf_geotransform_enable_flag … If the flag is false, the neural network based in-loop filter with geometric transformation on model input proposed in the present disclosure is not allowed to be used; see [0203]); and
when the first flag indicates to perform data rearrangement on the to-be-filtered information during filtering, decoding the bitstream to obtain a second flag indicating an index of a data rearrangement mode for the to-be-filtered information, and determining the first data processing mode from the plurality of data rearrangement modes based on the second flag (see [0136], If slice_nnlf_geotransform_flag is true, geometric transformation needs to be performed on the non-constant input of the model, and a type of the transformation is determined by slice_nnlf_geotransform_index … The corresponding geometric transformation type is selected according to the slice_nnlf_geotransform_index obtained by parsing the bitstream to perform geometric transformation on the reconstructed sample rec and the predicted sample pred).
As to claim 5, XIE further discloses wherein the determining the first data processing mode for to-be-filtered information comprises:
decoding a bitstream to obtain a third flag indicating the first data processing mode (see [0061]-[0063], Decode a bitstream to determine a related syntax element of a current coding tree unit … The related syntax element is used to indicate the geometric transformation type of the current coding tree unit); and
determining the first data processing mode from the plurality of data processing modes based on the third flag (see [0062]).
As to claim 6, XIE further discloses wherein the plurality of data rearrangement modes comprise at least one of the following: a rotation mode, a mode of horizontal flipping and then rotation, a mode of vertical flipping and then rotation, a mode of diagonal flipping and then rotation, a mode of downsampling and then rotation, and a mode of upsampling and then rotation (see [0090]).
As to claim 7, XIE further discloses wherein the rotation mode comprises any one of rotating left by (N*90)° or rotating right by (N*90)°, N being a positive integer (FIGS. 4-6).
As to claim 8, XIE further discloses wherein the to-be-filtered information further comprises at least one of a predicted picture block corresponding to the reconstructed picture block, a boundary strength picture block, a frame type of the reconstructed picture block, and quantization parameter information (see [0079] and [0081], where the reference sample information at least comprises predicted sample information and/or reconstructed sample information of the current coding tree unit).
As to claim 9, XIE further discloses wherein the performing data rearrangement on the reconstructed picture block according to the first data processing mode, to obtain rearranged to-be-filtered information comprises:
performing data rearrangement on the reconstructed picture block according to the first data processing mode, and performing data rearrangement on at least one of the predicted picture block, the boundary strength picture block, the frame type, and the quantization parameter information according to the first data processing mode, to obtain the rearranged to-be-filtered information (see [0089], Perform geometric transformation on the reference sample information of the current coding tree unit according to the geometric transformation type, to obtain geometric transformed reference sample information; see [0079], where the reference sample information at least comprises predicted sample information and/or reconstructed sample information of the current coding tree unit).
As to claim 10, XIE discloses an electronic device, comprising a processor and a memory (FIG. 12),
the memory being configured to store a computer program (see [0248]); and
the processor being configured to execute the computer program (see [0248]), and cause the electronic device to implement a neural network-based picture filtering method including:
determining a first data processing mode for to-be-filtered information (FIG. 3, step 302: Determine a geometric transformation type of the current coding tree unit according to the related syntax element; see [0064], it is determined whether a current image block in which the current coding tree unit is located uses a neural network based in-loop filtering technology with performing the geometric transformation on an input … or it is determined to apply another filtering technology), the to-be-filtered information comprising a reconstructed picture block (see [0079], [0081]);
when the first data processing mode is one of a plurality of data rearrangement modes (see [0090], the geometric transformation type includes one of the following: diagonal flip, horizontal flip, vertical flip, or rotation by a preset angle), performing data rearrangement on the reconstructed picture block according to the first data processing mode, to obtain rearranged to-be-filtered information (FIG. 3, step 304; see [0064], If it is determined, according to the first syntax element, to use the neural network based in-loop filtering technology with performing the geometric transformation on an input, the geometric transformation type is further determined; see [0089], Perform geometric transformation on the reference sample information of the current coding tree unit according to the geometric transformation type, to obtain geometric transformed reference sample information);
filtering the rearranged to-be-filtered information by using a neural network filter, to obtain a filtered picture block of the rearranged to-be-filtered information (FIG. 3, step 305; see [0103], Input the geometric transformed reference sample information of the current coding tree unit to a neural network based in-loop filter model for filtering, to output filtered reconstructed sample information); and
performing data inverse rearrangement on the filtered picture block of the rearranged to-be-filtered information according to the first data processing mode, to obtain a filtered reconstructed picture block (FIG. 3, step 306; see [0121], Perform inverse geometric transformation on the filtered reconstructed sample information according to the geometric transformation type, to obtain final reconstructed sample information of a current coding tree unit).
As to claims 11-18, claims 11-18 recite the same features as those recited in claims 2-9, respectively, and are therefore rejected for the same reasons as used above.
As to claim 19, XIE discloses a non-transitory computer-readable storage medium, configured to store a computer program and a bitstream, the computer program, when executed by a processor of a computer device (FIG. 12 and [0248]; see [0058]), enabling the computer device to perform a neural network-based picture filtering method including:
determining a first data processing mode for to-be-filtered information (FIG. 3, step 302: Determine a geometric transformation type of the current coding tree unit according to the related syntax element; see [0064], it is determined whether a current image block in which the current coding tree unit is located uses a neural network based in-loop filtering technology with performing the geometric transformation on an input … or it is determined to apply another filtering technology), the to-be-filtered information comprising a reconstructed picture block (see [0079], [0081]);
when the first data processing mode is one of a plurality of data rearrangement modes (see [0090], the geometric transformation type includes one of the following: diagonal flip, horizontal flip, vertical flip, or rotation by a preset angle), performing data rearrangement on the reconstructed picture block according to the first data processing mode, to obtain rearranged to-be-filtered information (FIG. 3, step 304; see [0064], If it is determined, according to the first syntax element, to use the neural network based in-loop filtering technology with performing the geometric transformation on an input, the geometric transformation type is further determined; see [0089], Perform geometric transformation on the reference sample information of the current coding tree unit according to the geometric transformation type, to obtain geometric transformed reference sample information);
filtering the rearranged to-be-filtered information by using a neural network filter, to obtain a filtered picture block of the rearranged to-be-filtered information (FIG. 3, step 305; see [0103], Input the geometric transformed reference sample information of the current coding tree unit to a neural network based in-loop filter model for filtering, to output filtered reconstructed sample information); and
performing data inverse rearrangement on the filtered picture block of the rearranged to-be-filtered information according to the first data processing mode, to obtain a filtered reconstructed picture block (FIG. 3, step 306; see [0121], Perform inverse geometric transformation on the filtered reconstructed sample information according to the geometric transformation type, to obtain final reconstructed sample information of a current coding tree unit).
As to claim 20, XIE further discloses wherein the method further comprises:
when the first data processing mode is a data non-rearrangement mode, directly filtering the to-be-filtered information by using the neural network filter, to obtain the filtered reconstructed picture block (see [0064], if it is determined not to use the neural network based in-loop filtering technology with performing the geometric transformation on an input, it is determined that the input is directed inputted to a neural network based in-loop filter model without being processed by the geometric transformation).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at 5712727331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482
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