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 14, 16-26, 29, 31-32 and 34-38 are pending.
Claims 1-13, 15, 27-28, 30 and 33 are canceled.
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) 14, 16-19, 23-24, 26, 29, 31-32, 34 and 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hitosugi (US20120288211) in view of Takahashi et al (US20040179609) and further in view of Park et al (US20180268571).
Regarding claim 14, 23 and 24, Hitosugi teaches an image compression apparatus comprising:
(Hitosugi, Fig. 1, an image processing apparatus 300”)
a target region extraction circuit configured to
divide an image into block images,
(Hitosugi, divides the raster image into tiles (block images); Fig. 6, “In step S802, the tile dividing unit 507 divides the raster image data into tiles”, [0062]; dividing input data into tiles, which are block images of a given area)
determine, for each of the block images, whether an object having a predetermined size within a predetermined range is included in the block image, including determining that the object having the predetermined size is included in one of the block images, and
(Hitosugi, perform per-tile determination between flat-fill (single color) and non-flat-fill (multi-color/bitmap) tiles as part of its region determination flow and subsequent compression branching, which maps to identifying tiles that include content/structure (as opposed to single-color fill); Fig. 6, “In step S804, the image region determination unit 508 determines whether the tile read by the tile dividing unit 507 in step S803 is a flat fill portion”, [0062], “If the image region determination unit 508 determines that the tile is a flat fill portion (YES in step S804), then in step S805, the image compression unit 511 performs lossless compression on color data”, [0063], “If the image region determination unit 508 determines that the tile is not a flat fill portion (i.e., the tile is a bitmap portion in present exemplary embodiment) (NO in step S804)… In step S807, the image compression unit 511 performs lossy compression on the image data”, [0065]; “if a divided tile has one single color, such a region is referred to as a flat fill portion. On the other hand, a region in which each of adjacent pixels inside a divided tile has a different color value is referred to as a bitmap portion”, [0061]; Takahashi makes explicit that a multi-color/boundary tile contains part of an object (ROI) within the tile); Fig. 5, “tile “A” is ROI boundary tile including ROI 10 and non-ROI 12, tile “B” is ROI tile composed of only ROI 10 and tile “C” is non-ROI tile composed of only non-ROI 12”, [0058]; Thus, Hitosugi’s non-flat-fill tiles correspond to tiles including one or more objects, and Takahashi’s boundary-tile example supplies the “predetermined size within a predetermined range” interpretation (e.g., at least two ROI pixels and fewer than N ROI pixels in a tile); the same boundary-tile identification may be used by Hitosugi’s edge detection unit 512 (Fig. 5) to identify object edges (a sub-object))
Therefore, 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 teachings of Takahashi into the system or method of Hitosugi in order to enable a simple edge detection method for image compression. The combination of Hitosugi and Takahashi also teaches other enhanced capabilities.
The combination of Hitosugi and Takahashi further teaches:
extract the one of the block images determined to include the object having the predetermined size as a target region; and
(Hitosugi, multi-color tiles (non-flat fill tiles), which are determined to contain objects such as edges, are selected for lossy compression, Fig. 6, steps S804, S807, [0063-0065]; This selection process effectively extracts these tiles as target regions for specialized processing; Takahashi supports this by identifying tile “A” (the ROI boundary tile) as a distinct region for processing due to its object content ([0058], Fig. 5). The combined teachings demonstrate that block images containing objects (e.g., edges or ROI boundaries) are extracted as target regions)
an image compression circuit configured to compress the image after determining a compression ratio for each of the block images based on an extraction result of the target region,
(Hitosugi, image compression unit 511 applies different compression techniques based on the tile classification result (Fig. 6, steps S805, S807, [0063], [0065]). Flat fill tiles undergo lossless compression, while non-flat fill tiles (containing objects) undergo lossy compression ([0061, 0063, 0065]); compression rate for the lossless compression is lower than that of lossy compression; this adaptive compression approach ensures that the compression ratio is tailored to the content of each block image, as determined by the target region extraction process; Takahashi’s identification of ROI boundary tiles (Fig. 5, [0058]) further supports the differentiation of tiles for compression, aligning with Hitosugi’s content-based compression strategy)
The combination of Hitosugi and Takahashi does not expressly disclose but Park teaches:
wherein the image compression circuit is configured to compress the image such that the compression ratio in the target region in the image is lower than the compression ratio in a region, in the image, other than the target region.
(Park, “the parameter adjusting unit may set a quantization parameter for the background region to a first value and set a quantization parameter for the object region to a second value smaller than the first value”, [0011]; the “object region” corresponds to the target region and the “background region” corresponds to the non-target region, with different quantization parameters assigned; “For example, as the quantization parameter is larger, a data loss for the image increases but a compression rate of the image may be enhanced. On the contrary, as the quantization parameter is smaller, the compression rate of the image decreases but image quality may be enhanced”, [0033]; this explicitly links smaller QP (used for the object/target region per the quote above) to decreased “compression rate,” i.e., less compression / lower compression ratio than the background region that uses a larger QP; “The image compression unit 130 may use a quantization parameter adjusted by the parameter processing unit 120 to compress the image IMG and output the compressed image IMG_C”, [0034]; this ties the region-specific QP setting to the actual compression operation performed by the compression unit/circuit)
Therefore, 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 teachings of Park into the modified system or method of Hitosugi and Takahashi in order to reduce the file size by assigning a high compression ratio to the portion of the image outside the region of interest while assigning a low compression ratio to the portion inside the region of interest. The combination of Hitosugi, Takahashi and Park also teaches other enhanced capabilities.
Regarding claim 16, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 14, wherein
the target region extraction circuit is configured to further extract a type of the object included in the target region, and
(Takahashi, see comments on claim 1; Fig. 5, tiles “A” including edges)
the image compression circuit is configured to further add information about the type of the object to the image that has been compressed.
(Takahashi, Fig. 5, obviously, color information, e.g., black and white, is typically part of the output of a compressed image/tile)
Regarding claims 17 and 29, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 14, wherein the target region extraction circuit is configured to extract a region including an object that has the predetermined size and that is of a type corresponding to a use of the image that has been compressed as the target region.
(Takahashi, see comment on claim 1; Fig. 5, require at least two ROI pixels (and < N ROI pixels) or a predetermined margin of pixels to indicate the existence of an object edge; more pixels are require to form a 2D shape such as a circle in an image)
Regarding claims 18 and 31-32, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 14, wherein the predetermined size differs depending on a type of the object.
(Takahashi, see comment on claim 17; Fig. 5, require at least two ROI pixels (and < N ROI pixels) for indicating existence of an object edge and more pixels for a 2D shape such as a circle in an image)
Regarding claim 19, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 14, wherein the image compression circuit is configured to compress the image at a compression ratio corresponding to a type of the object included in the target region.
(Takahashi, see comment on claim 1; besides the color ratio, different boundary shapes of the edge objects are subject to different compression ratios because of different data redundances)
Regarding claim 26, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches an image processing system comprising:
the image compression apparatus according to claim 14; and
an image decompression apparatus configured to acquire the image that has been compressed from the image compression apparatus and decompress the acquired image that has been compressed.
(Hitosugi, Fig. 3, “The image compression unit 511 performs image compression on each tile according to the selected compression method. The raster image data in each compressed tile is stored in the image storage area 506”, [0048], “The image decompression unit 515 decompresses raster image data of each tile stored in the image storage area 506”, [0050])
Regarding claim 34, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination further teaches an image compression apparatus according to claim 14, wherein the predetermined range specifies a minimum pixel count greater than zero and a maximum pixel count less than a total number of pixels included in each of the block images.
(Takahashi, Fig. 5; boundary line section in boundary tile A is an object, at least one ROI pixel (B type pixel) and less than than N ROI pixels in a tile (N is total number of pixels in each tile))
Regarding claim 38, the combination of Hitosugi, Takahashi, Park and Guan teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 14, wherein
the image compression circuit performs compression by quantizing frequency transform coefficients of the block images using a first quantization table for the target region and a second quantization table for block images other than the target region, and
(Takahashi, “a detector which detects an existence status of ROI within said compressed image data based on a frequency transform coefficient of said tile for every tile; a determiner which determines whether each tile is a ROI tile composed of only ROI, a non-ROI tile composed of only non-ROI, or a ROI boundary tile composed of ROI and non-ROI based on said existence status of ROI detected by said detector”, [abstract]; detecting ROI status of tiles and processing them differently, which relates to using different quantization for ROI versus non-ROI regions)
quantization coefficients of the first quantization table result in the lower compression ratio compared to quantization coefficients of the second quantization table.
(Park, see comments on claim 14 for details, [0011, 0033, 0034])
Claim(s) 20, 22, 25 and 36-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hitosugi (US20120288211) in view of Takahashi et al (US20040179609) and further in view of Park et al (US20180268571) and Guan (US2017/0270378).
Regarding claim 20, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination does not expressly disclose but Guan teaches the image compression apparatus according to claim 14, further comprising:
a target region prediction circuit configured to predict, on the basis of a target region extracted from a first image captured at a first time and on the basis of a second image captured at a second time different from the first time, a target region in the second image,
(Guan, Fig. 2; “When generating the signal-color recognition dictionary DC2, the signal-candidate-region recognizer 30 cuts out the signal-recognition-processing target region 82 from the sample image data acquired by the in-vehicle camera 12 as the learning image data”, [0112]; the image recognition for an instant input image from image acquirer 22 is based on the information stored in the signal-color recognition dictionary DC2 learned from the previously captured images; “The signal-shape recognizer 32 generates or updates the signal-recognition-processing target-region dictionary DC3 according to a learning method such as an SVM (Support Vector Machine) machine learning technique”, [0066])
Therefore, 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 teachings of Guan into the modified system or method of Hitosugi, Takahashi and Park in order to use machine learning for image recognition for better accuracy. The combination of Hitosugi, Takahashi, Park and Guan also teaches other enhanced capabilities.
The combination of Hitosugi, Takahashi, Park and Guan further teaches:
wherein the image compression circuit is configured to compress the second image on the basis of a result of the prediction by the target region prediction circuit.
(Hitosugi, Takahashi, see comments on claim 1)
Regarding claim 22, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination of Hitosugi, Takahashi, Park and Guan further teaches the image compression apparatus according to claim 14, wherein the image is captured by a camera mounted on a moving body.
(Guan, Figs. 1-2, “a vehicle 90 mounted with a recognition device 10”, [0052], “the recognition device 10 includes a camera 2”, [0053]; Fig.28, in-vehicle camera 12)
Regarding claim 25, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination of Hitosugi, Takahashi, Park and Guan further teaches an image compression system comprising:
a camera mounted on a moving body; and
the image compression apparatus according to claim 14, the image compression apparatus being configured to compress an image captured by the camera.
(Guan, see comments on claim 22; Fig.28, in-vehicle camera 12, compression/decompression circuit 68; “The compression/decompression circuit 68 compresses the image data output from the image processing circuit 50 and stores the image data in the memory card 70”, [0139])
Regarding claim 36, the combination of Hitosugi, Takahashi and Park teaches its/their respective base claim(s).
The combination of Hitosugi, Takahashi, Park and Guan further teaches the image compression apparatus according to claim 14, wherein the target region extraction circuit determines whether the object having the predetermined size within the predetermined range is included in the block image using a machine-learned model.
(Guan, “The signal-shape recognizer 32 generates or updates the signal-recognition-processing target-region dictionary DC3 according to a learning method such as an SVM (Support Vector Machine) machine learning technique”, [0066]; using a machine-learned model (SVM) to recognize and set the target region (object-recognition-processing target region) for objects like traffic lights)
Regarding claim 37, the combination of Hitosugi, Takahashi, Park and Guan teaches its/their respective base claim(s).
The combination further teaches the image compression apparatus according to claim 36, wherein the machine-learned model includes at least one of. a convolutional neural network (CNN), a recurrent neural network (RNN), or an AutoEncoder.
(Park, “an object extracting unit configured to perform convolution neural network (CNN) training and identify an object from an image received externally”, [0006])
Allowable Subject Matter
Claim(s) 21 and 35 is/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 Claim(s).
The following is a statement of reasons for the indication of allowable subject matter:
Claim(s) 21 and 35 recite(s) limitation(s) related to (1) steps of predicting a movement of a target region, and (2) using circumscribed-rectangle pixel-count square-root thresholds to detect objects. There are no explicit teachings to the above limitation(s) found in the prior art cited in this office action and from the prior art search.
Response to Arguments
Applicant's arguments filed on 11/24/2025 with respect to one or more of the pending claims have been fully considered but are moot in view of the new ground(s) of rejection.
Conclusion
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amandeep Saini can be reached on (571)272-3382. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JIANXUN YANG/
Primary Examiner, Art Unit 2662 1/29/2026