-
公开(公告)号：US20240346546A1
公开(公告)日：2024-10-17
申请号：US18584716
申请日：2024-02-22
Applicant: Google LLC
Inventor： Catherine Shyu , Luying Li , Feng Yang , Junjie Ke , Xiyang Luo , Hao Feng , Chao-Hung Chen , Wenjing Kang , Zheng Xia , Shun-Chuan Chen , Yicong Tian , Xia Li , Han Ke
IPC: G06Q30/0242
CPC classification number: G06Q30/0244
Abstract: Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.
-
公开(公告)号：US20240022760A1
公开(公告)日：2024-01-18
申请号：US18256837
申请日：2021-08-05
Applicant: Google LLC
Inventor： Yinxiao Li , Peyman Milanfar , Feng Yang , Ce Liu , Ming-Hsuan Yang , Pengchong Jin
IPC: H04N19/59 , G06T3/00 , H04N19/117 , G06V10/74 , H04N19/503 , H04N19/70 , H04N19/80
CPC classification number: H04N19/59 , G06T3/0093 , H04N19/117 , G06V10/761 , H04N19/503 , H04N19/70 , H04N19/80
Abstract: Example aspects of the present disclosure are directed to systems and methods which feature a machine-learned video super-resolution (VSR) model which has been trained using a bi-directional training approach. In particular, the present disclosure provides a compression-informed (e.g., compression-aware) super-resolution model that can perform well on real-world videos with different levels of compression. Specifically, example models described herein can include three modules to robustly restore the missing information caused by video compression. First, a bi-directional recurrent module can be used to reduce the accumulated warping error from the random locations of the intra-frame from compressed video frames. Second, a detail-aware flow estimation module can be added to enable recovery of high resolution (HR) flow from compressed low resolution (LR) frames. Finally, a Laplacian enhancement module can add high-frequency information to the warped HR frames washed out by video encoding.
-
公开(公告)号：US20240005563A1
公开(公告)日：2024-01-04
申请号：US18465352
申请日：2023-09-12
Applicant: Google LLC
Inventor： Innfarn Yoo , Xiyang Luo , Feng Yang
CPC classification number: G06T9/002 , G06T5/50 , G06N20/00 , G06N3/084 , G06T2207/10024 , G06T2207/20212
Abstract: Example embodiments allow for training of encoders (e.g., artificial neural networks (ANNs)) to facilitate dithering of images that have been subject to quantization in order to reduce the number of colors and/or size of the images. Such a trained encoder generates a dithering image from an input quantized image that can be combined, by addition or by some other process, with the quantized image to result in a dithered output image that exhibits reduced banding or is otherwise aesthetically improved relative to the un-dithered quantized image. The use of a trained encoder to facilitate dithering of quantized images allows the dithering to be performed in a known period of time using a known amount of memory, in contrast to alternative iterative dithering methods. Additionally, the trained encoder can be differentiable, allowing it to be part of a deep learning image processing pipeline or other machine learning pipeline.
-
公开(公告)号：US11854165B2
公开(公告)日：2023-12-26
申请号：US17922531
申请日：2020-05-19
Applicant: Google LLC
Inventor： Yilin Wang , Balineedu Adsumilli , Feng Yang
CPC classification number: G06T5/002 , G06T5/20 , G06T7/13 , G06V10/56 , G06V10/761 , G06T2207/20081
Abstract: A method includes training a first model to measure the banding artefacts, training a second model to deband the image, and generating a debanded image for the image using the second model. Training the first model can include selecting a first set of first training images, generating a banding edge map for a first training image, where the map includes weights that emphasize banding edges and de-emphasize true edges in the first training image, and using the map and a luminance plane of the first training image as input to the first model. Training the second model can include selecting a second set of second training images, generating a debanded training image for a second training image, generating a banding score for the debanded training image using the first model, and using the banding score in a loss function used in training the second model.
-
公开(公告)号：US11790564B2
公开(公告)日：2023-10-17
申请号：US16834857
申请日：2020-03-30
Applicant: Google LLC
Inventor： Innfarn Yoo , Xiyang Luo , Feng Yang
CPC classification number: G06T9/002 , G06N3/084 , G06N20/00 , G06T5/50 , G06T2207/10024 , G06T2207/20212
Abstract: Example embodiments allow for training of encoders (e.g., artificial neural networks (ANNs)) to facilitate dithering of images that have been subject to quantization in order to reduce the number of colors and/or size of the images. Such a trained encoder generates a dithering image from an input quantized image that can be combined, by addition or by some other process, with the quantized image to result in a dithered output image that exhibits reduced banding or is otherwise aesthetically improved relative to the un-dithered quantized image. The use of a trained encoder to facilitate dithering of quantized images allows the dithering to be performed in a known period of time using a known amount of memory, in contrast to alternative iterative dithering methods. Additionally, the trained encoder can be differentiable, allowing it to be part of a deep learning image processing pipeline or other machine learning pipeline.
-
Patent Agency Ranking
公开(公告)号：US11368697B1
公开(公告)日：2022-06-21
申请号：US15968302
申请日：2018-05-01
Applicant: GOOGLE LLC
Inventor： Haoran Man , Jingyu Cui , Abraham Stephens , Venkatesan Esakki , Pascal Massimino , Feng Yang , Cecilia Rabess
IPC: H04N19/136 , H04N19/124 , G06V10/98
Abstract: A method includes compressing an image using a quality setting, determining a quality of the compressed image based on a quality metric, and determining if the quality of the compressed image is within a quality range. In response to determining the quality of the compressed image is within the quality range, store the compressed image; and in response to determining the quality of the compressed image is not within the quality range, modify the quality setting, and repeat the compressing step with the modified quality setting, both determining steps, and the applicable in response to step.
-
公开(公告)号：US20210304445A1
公开(公告)日：2021-09-30
申请号：US16834857
申请日：2020-03-30
Applicant: Google LLC
Inventor： Innfarn Yoo , Xiyang Luo , Feng Yang
Abstract: Example embodiments allow for training of encoders (e.g., artificial neural networks (ANNs)) to facilitate dithering of images that have been subject to quantization in order to reduce the number of colors and/or size of the images. Such a trained encoder generates a dithering image from an input quantized image that can be combined, by addition or by some other process, with the quantized image to result in a dithered output image that exhibits reduced banding or is otherwise aesthetically improved relative to the un-dithered quantized image. The use of a trained encoder to facilitate dithering of quantized images allows the dithering to be performed in a known period of time using a known amount of memory, in contrast to alternative iterative dithering methods. Additionally, the trained encoder can be differentiable, allowing it to be part of a deep learning image processing pipeline or other machine learning pipeline.
-
公开(公告)号：US20250124537A1
公开(公告)日：2025-04-17
申请号：US18999336
申请日：2024-12-23
Applicant: Google LLC
Inventor： Junjie Ke , Feng Yang , Qifei Wang , Yilin Wang , Peyman Milanfar
Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.
-
公开(公告)号：US20250111280A1
公开(公告)日：2025-04-03
申请号：US18893594
申请日：2024-09-23
Applicant: Google LLC
Inventor： Xiaohang Li , Feng Yang , Fong Shen
IPC: G06N20/00
Abstract: One example method includes receiving, by an artificial intelligence (AI) system, a query; generating, by the AI system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the AI system, user feedback associated with the plurality of candidate digital components, each user feedback indicating a user preference level of a corresponding candidate digital component; obtaining, by the AI system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the AI system and based on the user feedback and the performance data, a candidate digital component of the plurality of candidate digital components; generating, by the AI system and based on the candidate digital component, training data; and refining, by the AI system, the machine learning model using the training data.
-
公开(公告)号：US20240087075A1
公开(公告)日：2024-03-14
申请号：US18027418
申请日：2022-01-11
Applicant: Google LLC
Inventor： Xiyang Luo , Feng Yang , Elnaz Barshan Tashnizi , Dake He , Ryan Matthew Haggarty , Michael Gene Goebel
CPC classification number: G06T1/0028 , G06T3/4046 , G06T7/0002 , G06T7/11 , G06T2201/0202 , G06T2207/20081 , G06T2207/20084 , G06T2207/30168
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating and decoding watermarks. An image and a data item is received. The encoder generates a first watermark and then a second watermark is generated using multiple first watermarks. The second watermark is used to watermark the image by overlaying the second watermark over the image. To decode the watermark, presence of a watermark is determined on a portion of an image. A distortion model determines distortions in the image and modifies the portion of the image based on the predicted distortions. The modified portion is decoded using the decoder to obtain a predicted first data item that is further used to validate the watermark based on the first data item.
-
-
-
-
-
-
-
-
-
Search Results
42
records
(took 0.017 seconds)
