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 .
Response to Amendment
Claims 2 and 16 have been added. Claims 1, 15, and 20 have been amended. Claims 21-22 have been added. Claims 1, 3-15, 17-22 are pending for examination.
Response to Arguments
Applicant's arguments filed 03/09/2026 have been fully considered but they are not persuasive.
Applicant argues that: Li fails to disclose, teach, or suggest selecting/select "for the component of the image block between using the refined version of the weighted prediction parameters or no weighted prediction parameters based on histogram distortions"
Examiner respectfully disagrees.
Claim 1 was amended to recite: “selecting for the component of the image block between using the refined version of the weighted prediction parameters or no weighted prediction parameters based on histogram distortions”. Examiner interprets the limitation as selecting between a refined prediction parameters and a non-refined parameters based on histogram distortions.
[0058] The weighted prediction parameter refinement module 260 operates when the refinement flag 254 indicates that refinement is enabled. In operation the weighted prediction parameter refinement module 260 is configured to generate a plurality of refined weighted prediction parameters 262 by refining the plurality of initial weighted prediction parameters 252. In an embodiment, the weighted prediction parameter refinement module 260 generates a joint intensity histogram for a first picture and a second picture, wherein the second picture is next in the sequence of pictures from the first picture.
The above cited portions of Li teach a refinement flag that indicates a no weighted prediction parameters. On the other hand, Li discloses refined weighted prediction parameters 262 are generated based on maximums determined from the joint intensity histogram. If the differences between the refined weighted prediction parameters 262 and the transform results are less than a detection threshold, there is no need for weighted prediction and the weighted prediction flag is set to indicate that weighted prediction is disabled. Otherwise the refined weighted prediction parameters 262 are output as the weighted prediction parameters 274 and the weighted prediction flag 272 is set to indicate that weighted prediction is enabled [0062].
Li teaches the weighted prediction flag is set to indicate that weighted prediction is disabled/enabled based on the refined weighted prediction parameters 262 generated based on maximums determined from the joint intensity histogram.
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.
Claims 1, 7-12, 14-15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li (US 20150195524 A1).
Regarding claim 1, Li teaches a method, comprising:
predicting a component of an image block of a video from a set of options that includes a first version of weighted prediction parameters, a refined version of the weighted prediction parameters, or no weighted prediction parameters (In step 404, the method considers if refinement is enabled. If so the method proceeds to step 406 to generate a plurality of refined weighted prediction parameters by refining the plurality of initial weighted prediction parameters and proceeding to step 408. Otherwise the method skips directly to step 408 to generate a weighted prediction flag that indicates one of: enable weighted prediction and disable weighted prediction, based on the refinement flag. [0063] Examiner’s note: Fig. 13: options shown in 406 and 408);
selecting for the component of the image block between using the refined version of the weighted prediction parameters or no weighted prediction parameters based on histogram distortions (Step 406 can include generating a joint intensity histogram for a first picture and a second picture, wherein the second picture is next in the sequence of pictures from the first picture. The method can further include detecting a flash condition based on the joint intensity histogram. [0064] Examiner’s note: Fig. 13: options and selection shown in 406 and 408); and
encoding the image block (Fig. 14: Video encoder/decoder).
Regarding claim 7, Li teaches the method of claim 1, further comprising selecting for the component of the image block between using the first version of the weighted prediction parameters or no weighted prediction parameters based on a sum of absolute differences (the first parameter is generated based on a first sum of absolute differences between pixel data and pixel mean data for a first picture and a second sum of absolute differences between pixel data and pixel mean data for a second picture. The refinement flag 254 can be generated by comparing the plurality of initial weighted prediction parameters 252 to a corresponding plurality of weighted prediction parameter values. [0053]).
Regarding claim 8, Li teaches the method of claim 7, wherein selecting for the component of the image block between using the first version of the weighted prediction parameters or no weighted prediction parameters based on a sum of absolute differences comprises obtaining a sum of absolute differences between samples of the image and samples of a scaled reference image, and obtaining a sum of absolute differences between samples of the image and samples of the reference image (the first parameter is generated based on a first sum of absolute differences between pixel data and pixel mean data for a first picture and a second sum of absolute differences between pixel data and pixel mean data for a second picture. The refinement flag 254 can be generated by comparing the plurality of initial weighted prediction parameters 252 to a corresponding plurality of weighted prediction parameter values. [0053]).
Regarding claim 9, Li teaches the method of claim 1, further comprising determining whether or not a refining of the first version of weighted prediction parameters is done to obtain the refined version of the weighted prediction parameters (In step 404, the method considers if refinement is enabled. [0063]).
Regarding claim 10, Li teaches the method of claim 9, wherein the determining is based on a configuration parameter of an encoder (generate a refinement flag 254 that indicates whether parameter refinement is enabled or disabled. [0052]).
Regarding claim 11, Li teaches the method of claim 9, wherein when the first version of the weighted prediction parameters is obtained from default weighted prediction parameters, it is determined that the first version of weighted prediction parameters is refined to obtain the refined version of weighted prediction parameters (the weighted prediction parameter generation module 250 is configured to generate a plurality of initial weighted prediction parameters 252, to analyze the plurality of initial weighted prediction parameters, and to generate a refinement flag 254 that indicates whether parameter refinement is enabled or disabled. [0052]).
Regarding claim 12, Li teaches the method of claim 1, wherein the first version of the weighted prediction parameters is obtained either from default weighted prediction parameters or based on samples of the image and samples of a reference image (The plurality of initial weighted prediction parameters 252 can be generated based on picture data 245--by considering two successive pictures of the sequence of pictures [0052]).
Regarding claim 14, Li teaches the method of claim 1, wherein the refined version of the weighted prediction parameters is obtained based on a scaled reference image histogram derived from samples of a reference image, samples of the image and the first version of the weighted prediction parameters (Fig. 5: motion search module 204: motion search module 204 can operate by downscaling incoming pictures and reference pictures to generate a plurality of downscaled pictures. [0026]).
Regarding claim 15, Li teaches an apparatus, comprising one or more processors configured to: predict a component of an image block of a video from a set of options that includes a first version of weighted prediction parameters, a refined version of the weighted prediction parameters or no weighted prediction parameters (In step 404, the method considers if refinement is enabled. If so the method proceeds to step 406 to generate a plurality of refined weighted prediction parameters by refining the plurality of initial weighted prediction parameters and proceeding to step 408. Otherwise the method skips directly to step 408 to generate a weighted prediction flag that indicates one of: enable weighted prediction and disable weighted prediction, based on the refinement flag. [0063] Examiner’s note: Fig. 13: options shown in 406 and 408);
select for the component of the image block between using the refined version of the weighted prediction parameters or no weighted prediction parameters based on histogram distortions (Step 406 can include generating a joint intensity histogram for a first picture and a second picture, wherein the second picture is next in the sequence of pictures from the first picture. The method can further include detecting a flash condition based on the joint intensity histogram. [0064] Examiner’s note: Fig. 13: options and selection shown in 406 and 408); and
encode the image block (Fig. 14: Video encoder/decoder).
Regarding claim 18, Li teaches the apparatus of claim 15, wherein the one or more processors are configured to determine whether or not a refining of the first version of weighted prediction parameters is done to obtain the refined version of the weighted prediction parameters (In step 404, the method considers if refinement is enabled. [0063]).
Regarding claim 19, Li teaches the apparatus of claim 18, wherein when the first version of the weighted prediction parameters is obtained from default weighted prediction parameters, it is determined that the first version of weighted prediction parameters is refined to obtain the refined version of weighted prediction parameters (the weighted prediction parameter generation module 250 is configured to generate a plurality of initial weighted prediction parameters 252, to analyze the plurality of initial weighted prediction parameters, and to generate a refinement flag 254 that indicates whether parameter refinement is enabled or disabled. [0052]).
Regarding claim 20, Li teaches a non-transitory computer readable medium having stored instructions which, when executed by one or more processors ([0021] The video encoder/decoder 102 includes a processing module 200 that can be implemented using a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor), cause the one or more processors to:
predict a component of an image block of a video from a set of options that includes a first version of weighted prediction parameters, a refined version of the weighted prediction parameters, or no weighted prediction parameters (In step 404, the method considers if refinement is enabled. If so the method proceeds to step 406 to generate a plurality of refined weighted prediction parameters by refining the plurality of initial weighted prediction parameters and proceeding to step 408. Otherwise the method skips directly to step 408 to generate a weighted prediction flag that indicates one of: enable weighted prediction and disable weighted prediction, based on the refinement flag. [0063] Examiner’s note: Fig. 13: options shown in 406 and 408);
select for the component of the image block between using the refined version of the weighted prediction parameters or no weighted prediction parameters based on histogram distortions (Step 406 can include generating a joint intensity histogram for a first picture and a second picture, wherein the second picture is next in the sequence of pictures from the first picture. The method can further include detecting a flash condition based on the joint intensity histogram. [0064] Examiner’s note: Fig. 13: options and selection shown in 406 and 408); and
encode the image block (Fig. 14: Video encoder/decoder).
Allowable Subject Matter
Claims 3-6, 13, 17 and 21-22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HESHAM K ABOUZAHRA whose telephone number is (571)270-0425. The examiner can normally be reached M-F 8-5.
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/HESHAM K ABOUZAHRA/ Primary Examiner, Art Unit 2486