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)(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 1-6, 8-16, and 17-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Elron (US 2024/0046427).
As per claims 1 and 10, Elron teaches, an image compression system and method, comprising: a memory having a frame buffer (Elron, fig.3 310 the frames go into here); a temporal noise reduction (TNR) circuit configured to receive a frame from an image sensor (Elron, ¶[0015] “In some examples, the use-case can be a specific camera module.” Represents image sensor and TNR represents temporal noise reduction (TNR) circuit configured to receive a frame from an image sensor as input frame ) and blend the frame with a decompressed reference frame from the frame buffer into a temporal noise reduction frame (Elron, fig.3 306 previous output would be decompressed reference frame, and that is from 308 which then goes back to the buffer 310 ); a compression circuit coupled to the TNR circuit, configured to receive the temporal noise reduction frame from the TNR circuit and compress the temporal noise reduction frame into a compressed reference frame utilizing a ratio (Elron, fig.6 Blend control utilizes a ratio, ¶[0029] “In some examples, the input to a TNR is a current input frame and a previous output frame, where a previous output frame is a blend of multiple previous input frames.” Since it blends multiple frames), wherein the compressed reference frame is stored in the frame buffer, and wherein the compression circuit is further configured to decompress the compressed reference frame in the frame buffer by the ratio as the decompressed reference frame (Elron, ¶[0045] “In various examples, the blend factor α is content dependent, such that regions in the frame that are similar to the previous frame (after rectification) will have a high blend factor α. Similarly, regions in which the current frame is different from the previous frame will have a low blend factor α. For example, a region that was occluded in a previous frame and is revealed in the current input frame, due to motion of an object, will have a blend factor α equal to about zero. Thus, in the equation above, “out” can be a portion of the output frame with the “in” and “prev_out” representing corresponding portions of the input frame and previous output frame. Note that TNRs can include additional features, such as motion compensation of the previous output to rectify it with the current view.” The different blends end up with different ratios, and the buffering and un buffering to blend would then the compressed and uncompressed); and a controller configured to adaptively adjust the ratio if parallel executing at least two programs (Elron, fig.2 at least two programs going and they are in parallel from 202a to 202h, and this ratio changes based on the actual images. These are the two programs).
As per claims 2 and 11, Elron teaches, the image compression system of claim 1, wherein the TNR circuit is further configured to perform weighting calculation on pixel data of the frame and pixel data of the decompressed reference frame, so as to obtain the temporal noise reduction frame (Elron, ¶[0048] “In particular, a ghost artifact appears when pixels of the moving foreground object are blended with the background pixels, making the moving object appear transparent. When the current input is blended with the previous output, the ghost artifact trails behind the moving object.” This represents the weighted calculation so that moving objects then appear).
As per claims 3 and 12, Elron teaches, the image compression system of claim 2, wherein the image compression system further comprises a spatial noise reduction (2DNR) circuit configured to receive the temporal noise reduction frame from the TNR circuit and perform weighting calculation to process the temporal noise reduction frame into a spatial noise reduction frame (Elron, fig.4A-B represents temporal noise reduction frame into a spatial noise reduction frame, as seen in fig.3 by bringing in the previous frames).
As per claims 4 and 13, Elron teaches, the image compression system of claim 3, wherein the 2DNR circuit is further configured to perform weighting calculation on pixel data and surrounding pixel data of the temporal noise reduction frame, so as to obtain the spatial noise reduction frame (Elron, ¶[0065] “The input image frame can be a still image from the video camera feed. The input image frame can include a matrix of pixels, each pixel having a color, lightness, and/or other parameter.” All of the pixel data is then considered).
As per claims 5 and 14, Elron teaches, the image compression system of claim 4, wherein the TNR circuit is further configured to determine whether each pixel in the frame is a still pixel or a moving pixel, so as to obtain a pixel status result (Elron, ¶[0048] “In particular, a ghost artifact appears when pixels of the moving foreground object are blended with the background pixels, making the moving object appear transparent. When the current input is blended with the previous output, the ghost artifact trails behind the moving object.” And ¶[0065] “The input image frame can be a still image from the video camera feed. The input image frame can include a matrix of pixels, each pixel having a color, lightness, and/or other parameter.” Represents the ability to tell what exactly the pixel is).
As per claims 6 and 15, Elron teaches, the image compression system of claim 5, wherein each of the TNR circuit and the 2DNR circuit performs weighting calculation based on the pixel status result (Elron, ¶[0030] “ In some examples, the difference can be the different between corresponding output frames in the video processed by the DNN with time-forward temporal noise reduction at the TNR 120 and the video processed by the DNN with time-reversal temporal noise reduction at the TNR 120. In some examples, the difference between corresponding output frames can be measured as the number of pixels in the corresponding output frames that are different from each other. In some examples, the difference between corresponding output frames can be measured using a loss function, as described below.” This represents TNR circuit and the 2DNR circuit performs weighting calculation based on the pixel status result with fig.2 TNR 204a and TNR 204b showing the two different units).
As per claims 8 and 17, Elron teaches, the image compression system of claim 1, wherein the controller is further configured to increase the ratio to release a part of the memory in response to the at least two programs needing to be executed (Elron, fig.4C if the frame pixels backwards and forward end up being the same to cancel each other out at those specific times, then this results in the memory being let go).
As per claims 9 and 18, Elron teaches, the image compression system of claim 8, wherein the at least two programs comprises at least one video program and at least one computer vision/neural network (CV/NN) program, and wherein the controller is further configured to utilize the part of the memory to execute the at least one CV/NN program (Elron, ¶ [0097] “Example 15 provides an apparatus, comprising: a computer processor for executing computer program instructions; and a non-transitory computer-readable memory storing computer program instructions executable by the computer processor to perform operations” This represents at least one computer vision program which then utilizes the memory).
Allowable Subject Matter
Claims 7 and 16 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. The limitations “wherein the controller utilizes a first noise reduction (NR) setting for noise reduction in response to the ratio decreasing, and wherein the controller utilizes a second NR setting for noise reduction in response to the ratio increasing, wherein the first NR setting is composed of a first TNR setting and a first 2DNR setting, and wherein the second NR setting is composed of a second TNR setting and a second 2DNR setting.” were not found in the prior art. While the step can be found separately in different prior arts, the ratio going up or down then having different settings in the first and second 2DNR was not found in the prior art in combination with the rest of the dependent subject matter.
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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