DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending.
Prior art:
D1: US20070183661A1 El-Maleh et al
D2: US20110235706A1 Demircin et al
D3: US20180225522A1 Molina et al
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-5, 7-15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D2.
Regarding claims 1 and 11, D1 teaches an image processing method comprising:
(D1, "The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence", [0007])
analyzing an unprocessed image to determine at least one region of interest within the unprocessed image and dynamically adjusting a number or a size of the at least one region of interest before splitting the unprocessed image into a first region corresponding to the at least one region of interest and a second region different from the at least one ROI;
(D1, "The size, shape and position of ROI object 24 may be fixed or adjustable, and may be defined, described or adjusted in a variety of ways.", [0048]; "system 14 then determines if there is more than one foreground object and merges multiple foreground objects together", [0044]; "ROI object segmentation enables selected foreground objects of a video sequence that may be of interest to a viewer to be extracted from the background of the video sequence.", [0002]; D2, "A guard band is required around the ROI to include non-skin areas as part of the ROI.", "This guard band is proportional to the shape/size of the ROI.", [0063]; D1 teaches analyzing a video frame to determine an ROI, dynamically adjusting its size or number, and splitting/extracting the ROI from the background; D2 teaches dynamically adjusting the ROI size by adding a guard band proportional to the ROI before processing; together D1 and D2 teaches dynamically adjusting the ROI size to accurately encompass boundary pixels before splitting the image; incorporating D2 into D1 provides a specific, dynamic guard band sizing algorithm to ensure all relevant ROI features are fully enclosed before separation)
The combination of D1 and D2 further teaches:
applying a first image processing algorithm having a greater computation power to the first region for acquiring a first processed result;
(D1, "The video sequence encoder may allocate more resources to the segmented ROI object to code the ROI object with higher quality", [0004]; "allocate additional coding bits to the ROI object of the video frame", [0037]; D2, "the video encoder prioritizes the ROI areas and encodes them at higher fidelity", [0022]; D1 teaches applying a processing algorithm allocating more resources and additional coding bits (greater computation power) to the ROI for higher quality; D2 teaches prioritizing ROI areas to encode them at higher fidelity; together D1 and D2 teaches applying an algorithm with greater computational requirements to the first region; incorporating D2 into D1 provides a specific rate-distortion optimization framework to achieve this higher fidelity processing)
applying a second image processing algorithm different from the first image processing algorithm and having a lower computation power than the first image processing algorithm to the second region for acquiring a second processed result; and
(D1, "allocate a reduced number of coding bits to non-ROI areas of the video frame.", [0037]; D2, "distortion in the ROI area to a factor of α1 lesser than the distortion in the non-ROI area we can ensure that ROI area is represented with higher fidelity than the non-ROI area.", [0047]; D1 teaches applying a different processing algorithm that allocates reduced bits (lower computation power) to non-ROI areas; D2 teaches mathematically setting the distortion of the non-ROI area higher than the ROI via different quantization; together D1 and D2 teaches applying a second, different processing algorithm with lower computation power to the second region; incorporating D2 into D1 provides a mathematically defined lower-fidelity quantization scale calculation for the non-ROI areas to efficiently manage overall bit constraints)
generating a processed image via the first processed result and the second processed result.
(D1, "The encoded video frame may then be transmitted over a wired or wireless communication channel to another communication device.", [0037]; D2, "compressed bit streams of the image are generated at step 340.", [0061]; D1 teaches generating a final processed image comprising the preferentially encoded regions; D2 teaches generating compressed bit streams of the processed image; together D1 and D2 teaches generating the processed image via the first and second processed results)
Regarding claims 2 and 12, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
defining a remaining region inside the unprocessed image outside the at least one region of interest as the second region, or defining all region inside the unprocessed image as the second region.
(D1, "The ROI object may be referred to as a “foreground” object within a video frame and non-ROI areas may be referred to as “background” areas within the video frame.", [0033]; "The selected regions may be considered foreground regions and unselected regions may be considered background regions.", [0089]; defining the remaining unselected regions outside the at least one region of interest as the second (background) region)
Regarding claims 3 and 13, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
increasing a first image quality of the first region by the first image processing algorithm to acquire the first processed result.
(D1, “... code the ROI object with higher quality for transmission to a recipient”, [0030]; increasing the image quality of the first region; “Accordingly, preferential allocation of coding bits to ROI objects can be helpful in improving the visual quality of the ROI object ...”, [0037]; improving the visual quality of the first region)
Regarding claims 4 and 14, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 3, further comprising:
maintaining a second image quality of the second region by the second image processing algorithm to acquire the second processed result.
(D2, Fig. 2; “De-blocking filter 270 operates to remove visual artifacts that may be present in the reconstructed macro-blocks received on path 267. The artifacts may be introduced in the encoding process due, for example, to the use of different modes of encoding. Artifacts may be present, for example, at the boundaries/edges of the received macro-blocks, and de-blocking filter 270 operates to smoothen the edges of the macro-blocks to improve visual quality”; [0034]; De-blocking filter 270 may be the second image processing algorithm that make the quality of the ROI objects and the non-ROI objects in the image better than per-filtering; based on the filtering characteristics, the filtered results may be at least maintaining or enhancing the quality of the non-ROI objects)
Regarding claims 5 and 15, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 3, further comprising:
enhancing a second image quality of the second region by the second image processing algorithm to acquire the second processed result, wherein the first image quality is greater than or different from the second image quality.
(D2, see comments on claim 4)
Regarding claims 7 and 17, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
adjusting the number or the size of the first region in accordance with a preset condition corresponding to computation constraint of an operation device or a target feature within the unprocessed image.
(D1, "System 14 may determine a computational complexity of the received frame", "decide to perform intra-mode segmentation when the computational complexity is above a pre-determined level", [0057]; D2, "Geometric techniques are used to determine face of male, female or child and appropriately calculate the guard bands needed.", [0063]; D1 teaches adjusting processing based on a preset computational constraint; D2 teaches calculating/adjusting the size of the ROI based on a target feature (face type); together D1 and D2 teaches adjusting the size of the first region in accordance with a target feature; incorporating D2 into D1 provides the specific geometric technique to dynamically size the ROI based on detected facial features)
Regarding claims 9 and 19, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 7, wherein the preset condition is the ever-changing computation constraint, and the image processing method adjusts the first region in accordance with a manually-input control command or a control command automatically analyzed by the preset condition.
(D1, “System 14 may determine a computational complexity of the received frame (46) ... Therefore, system 14 may decide to perform intra-mode segmentation when the computational complexity is above a pre-determined level “, [0057]; analyzing ever-changing computation constraint (frame complexity) automatically to adjust process, affecting region handling)
Regarding claims 10 and 20, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination further teaches the image processing method of claim 1, further comprising:
utilizing a smooth algorithm to adjust pixel values across a boundary between the first processed result and the second processed result.
(D2, "de-blocking filter 270 operates to smoothen the edges of the macro-blocks to improve visual quality.", [0034]; "Within the guard band the quantization scale is varied gradually from QROI to Qnon — ROI.", [0064]; D2 teaches utilizing a smoothing algorithm (gradually varying quantization and a de-blocking filter) to adjust pixel values across the boundary between the ROI and non-ROI regions; incorporating D2 into D1 provides a specific smoothing mechanism at the region boundaries to prevent abrupt quality changes and visual artifacts)
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over D1 in view of D2 and further in view of D3.
Regarding claims 6 and 16, the combination of D1 and D2 teaches its/their respective base claim(s).
The combination does not expressly disclose but D3 teaches the image processing method of claim 1, further comprising:
setting the first processed result acquired by the first image processing algorithm applied to the first region as prior information; and
the second image processing algorithm enhancing an image quality of the second region in accordance with the prior information to acquire the second processed result.
(D3, Fig. 5; “1 - Calculate the histogram of the input image pixels contained in region of interest pIr (i) and use this information to obtain the corresponding transformation function Tr; 2 - Apply this transformation Tr to the entire input image I or sub-region ro of the input image”, [0057]; D3 teaches this step-by-step: (1) first Region (ROI): It identifies a specific "region of interest" (r); (2) first Algorithm & Result (Prior Information): it analyzes only that ROI (the first algorithm) to calculate a specific "transformation function Tr" (the first result); this function represents the ideal contrast settings for the important part of the image; (3)second Region & Enhancement: It then takes that function (Tr) and applies it to the rest of the image or a larger area (ro). This means the background (second region) is enhanced using the specific "prior information" learned from the ROI)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to incorporate the histogram-based enhancement technique of D3 into the ROI-based video system of D1 and D2 in order to improve the overall visual consistency and contrast of the video frame; specifically, applying the transformation function derived from the statistical analysis of the priority ROI (first region) to the background (second region) ensures that the background remains visually coherent with the subject of interest, thereby enhancing the perceptual quality for the viewer in surveillance or video telephony applications without requiring independent, computationally intensive analysis of the background statistics. The combination of D1, D2 and D3 also teaches other enhanced capabilities.
Response to Arguments
Applicant's arguments filed on 7/7/2026 with respect to one or more of the pending claims have been fully considered but they are not persuasive.
Regarding claim(s) 1, Applicant, in the remarks, argues that the combination of the cited reference(s) fails to teach the newly amended limitations in the claims.
The Examiner respectfully disagreed. The office action has been updated to address applicant’s argument. See the updated review comments for details.
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 JIANXUN YANG whose telephone number is (571)272-9874. The examiner can normally be reached on MON-FRI: 8AM-5PM Pacific Time.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 8/22/2026
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