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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., “No-Reference Image Quality Assessment by Hallucinating Pristine Features” in view of Prabhakaran et al., “Image Quality Assessment using Semi-Supervised Representation Learning”.
Regarding claim 1, Chen discloses an image quality assessment (IQA) method (Abstract; a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination), comprising the steps of:
a) providing a pristine image (Fig. 1; Section III. The Proposed Scheme; a pristine reference image), and a distorted image related to the pristine image (Fig. 1; Section 3. The Proposed Scheme; a distorted image);
b) constructing (Fig. 1; Section III, A. PR Feature Learning, first and third paragraphs; the FR is constructed from an FR model based on the quality-embedding feature extractor);
c) finding, (Fig. 1; Section III, A. PR Feature Learning, first and third paragraphs; generating the distortion feature FD by the quality embedding feature extractor); and
d) constructing a pseudo-reference feature of the distorted feature (Abstract; Fig. 1; Section III, A. PR Feature Learning, first and third paragraphs; generating the pseudo-reference feature FPR from the distorted image).
Chen discloses claim 1 as enumerated above, but Chen does not explicitly disclose an equal-quality space as claimed.
However, Prabhakaran discloses an image encoder to cluster images based on the image quality using synthetically distorted versions of pristine unlabeled images. Images of similar quality are grouped closer in embedding space (Abstract; Section 2.2. Contrastive Representation Learning).
Therefore, taking the combined disclosures of Chen and Prabhakaran as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the image quality using synthetically distorted versions of pristine unlabeled images. Images of similar quality are grouped closer in embedding space as taught by Prabhakaran into the invention of Chen for the benefit of achieving superior performance on both synthetically and authentically distorted IQA datasets (Prabhakaran: Abstract).
Regarding claim 2, the IQA method of claim 1, Chen and Prabhakaran in the combination further disclose wherein Step b) further comprises:
e) estimating a near-threshold map of a feature extracted from the pristine image (Chen: Fig. 1; Section III, A. PR Feature Learning, first paragraph and Fig. 5; Section IV. EXPERIMENTAL RESULTS, D. Feature Visualization, second paragraph); and
f) constructing the equal-quality space under a guidance of the near-threshold map (Prabhakaran: Abstract; Section 2.2. Contrastive Representation Learning).
Regarding claim 3, the IQA method of claim 2, Chen in the combination further disclose wherein Step e) further comprises:
g) predicting the near-threshold map based on a global spatial correlation map and a local spatial correlation map (Fig. 1; Section III, A. PR Feature Learning, first paragraph and Fig. 5; Section IV. EXPERIMENTAL RESULTS, D. Feature Visualization, second paragraph).
Regarding claim 4, the IQA method of claim 3, Chen in the combination further disclose comprising, before Step g), steps of:
h) calculating a global standard deviation of the feature extracted from the pristine image (Fig. 1; Section III, A. PR Feature Learning, first paragraph);
i) calculating a local standard deviation of the feature extracted from the pristine image (Fig. 1; Section III, A. PR Feature Learning, first paragraph); and
j) generating the global and local spatial correlation maps based on the global and local standard deviations (Fig. 5; Section IV. EXPERIMENTAL RESULTS, D. Feature Visualization, second paragraph).
Regarding claim 5, the IQA method of claim 1, Chen in the combination further disclose wherein Step c) further comprises locating the best reference of the distorted feature within the equal-quality space in an element-wise minimum distance search manner (Fig. 1; Section III, A. PR Feature Learning, forth-fifth paragraphs).
Regarding claim 6, the IQA method of claim 1, Chen in the combination further disclose comprising a step of optimizing the constructed equal-quality space using at least one of a quality regression loss, a disturbance maximization loss and a content loss (Fig. 1; Section III, A. PR Feature Learning, third paragraph).
Regarding claim 7, the IQA method of claim 5, Chen in the combination further disclose wherein the step of optimizing the constructed equal-quality space uses all of the quality regression loss, the disturbance maximization loss and the content loss (Fig. 1; Section III, A. PR Feature Learning, third paragraph).
Regarding claim 8, the IQA method of claim 1, Prabhakaran in the combination further disclose wherein in Step b) the equal-quality space is constructed using a pre-trained artificial neural network (Section 1. Introduction, third paragraph).
Regarding claim 9, the IQA method of claim 8, Prabhakaran in the combination further disclose wherein Step c) is performed at every layer of the artificial neural network (Section 1. Introduction, third paragraph).
Regarding claim 10, the IQA method of claim 1, Chen in the combination further disclose comprising a step of predicting a quality score based on the distorted feature and the pseudo-reference feature (Fig. 1; Section III. The proposed Scheme and Section III, B. GRU-Based Quality Aggregation, first paragraph).
Regarding claim 11, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Regarding claim 12, this claim recites substantially the same limitations that are performed by claim 1 above, and it is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Marchesotti et l., US 8,712,157 discloses a computer-implemented system and method for predicting an image quality of an image.
Wang et al., US 10,540,589 discloses a system for image quality assessment of non-aligned images.
Chen et al., US 2014/0044384 discloses method, software, and computer for assessing the quality of an image.
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/VAN D HUYNH/Primary Examiner, Art Unit 2665