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公开(公告)号：US12254603B2
公开(公告)日：2025-03-18
申请号：US17908068
申请日：2020-05-19
Applicant: GOOGLE LLC
Inventor： Mohammad Izadi , Pavan Madhusudanarao , Balineedu Adsumilli
Abstract: Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.
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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.
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公开(公告)号：US20230119747A1
公开(公告)日：2023-04-20
申请号：US17908068
申请日：2020-05-19
Applicant: GOOGLE LLC
Inventor： Mohammad Izadi , Pavan Madhusudanarao , Balineedu Adsumilli
Abstract: Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.
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公开(公告)号：US20230101806A1
公开(公告)日：2023-03-30
申请号：US17908352
申请日：2020-05-19
Applicant: Google LLC
Inventor： Sam John , Balineedu Adsumilli , Akshay Gadde
IPC: H04N19/40 , H04N19/184 , H04N19/119 , H04N19/192 , H04N19/147
Abstract: A learning model is trained for rate-distortion behavior prediction against a corpus of a video hosting platform and used to determine optimal bitrate allocations for video data given video content complexity across the corpus of the video hosting platform. Complexity features of the video data are processed using the learning model to determine a rate-distortion cluster prediction for the video data, and transcoding parameters for transcoding the video data are selected based on that prediction. The rate-distortion clusters are modeled during the training of the learning model, such as based on rate-distortion curves of video data of the corpus of the video hosting platform and based on classifications of such video data. This approach minimizes total corpus egress and/or storage while further maintaining uniformity in the delivered quality of videos by the video hosting platform.
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公开(公告)号：US20220222784A1
公开(公告)日：2022-07-14
申请号：US17708983
申请日：2022-03-30
Applicant: Google LLC
Inventor： Damien Kelly , Neil Birkbeck , Balineedu Adsumilli , Mohammad Izadi
IPC: G06T5/00
Abstract: Processing a spherical video using denoising is described. Video content comprising the spherical video is received. Whether a camera geometry or a map projection, or both, used to generate the spherical video is available is then determined. The spherical video is denoised using a first technique responsive to a determination that the camera geometry, the map projection, or both is available. Otherwise, the spherical video is denoised using a second technique. At least some steps of the second technique can be different from steps of the first technique. The denoised spherical video can be encoded for transmission or storage using less data than encoding the spherical video without denoising.
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Patent Agency Ranking
公开(公告)号：US11308584B2
公开(公告)日：2022-04-19
申请号：US16613932
申请日：2017-12-04
Applicant: Google LLC
Inventor： Damien Kelly , Neil Birkbeck , Balineedu Adsumilli , Mohammad Izadi
IPC: G06T5/00
Abstract: A method for denoising video content includes identifying a first frame block associated with a first frame of the video content. The method also includes estimating a first noise model that represents characteristics of the first frame block. The method also includes identifying at least one frame block adjacent to the first frame block. The method also includes generating a second noise model that represents characteristics of the at least one frame block adjacent to the first frame block by adjusting the first noise model based on at least one characteristic of the at least one frame block adjacent to the first frame block. The method also includes denoising the at least one frame block adjacent to the first frame block using the second noise model.
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公开(公告)号：US20210287341A1
公开(公告)日：2021-09-16
申请号：US16613945
申请日：2017-12-05
Applicant: Google LLC
Inventor： Neil Birkbeck , Balineedu Adsumilli , Mohammad Izadi
IPC: G06T5/00
Abstract: A method for denoising video content includes identifying a first frame block of a plurality of frame blocks associated with a first frame of the video content. The method also includes determining an average intensity value for the first frame block. The method also includes determining a first noise model that represents characteristics of the first frame block. The method also includes generating a denoising function using the average intensity value and the first noise model for the first frame block. The method further includes denoising the plurality of frame blocks using the denoising function.
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公开(公告)号：US20210240431A1
公开(公告)日：2021-08-05
申请号：US16779921
申请日：2020-02-03
Applicant: GOOGLE LLC
Inventor： Marcin Gorzel , Balineedu Adsumilli
Abstract: Assigning spatial information to audio segments is disclosed. A method includes receiving a first audio segment that is non-spatialized and is associated with first video frames; identifying visual objects in the first video frames; identifying auditory events in the first audio segment; identifying a match between a visual object of the visual objects and an auditory event of the auditory events; and assigning a spatial location to the auditory event based on a location of the visual object.
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公开(公告)号：US10999578B2
公开(公告)日：2021-05-04
申请号：US16612889
申请日：2017-12-12
Applicant: Google LLC
Inventor： Chao Chen , Balineedu Adsumilli , Shawn Singh , Yilin Wang
IPC: H04N19/124 , H04N19/103 , H04N19/154 , H04N19/172 , H04N19/176 , H04N19/61
Abstract: Systems and methods for transcoding media content are disclosed. In some embodiments, the method includes obtaining a transcoded media content that is transcoded from an uploaded media content. The method includes determining a plurality of degradation metric values corresponding to the transcoded media content based on the uploaded media content and the transcoded media content, each degradation metric value corresponding to a different degradation metric type. The method includes mapping each degradation metric value to a respective calibrated score to obtain a plurality of calibrated scores. The method includes determining an aggregated quality score of the transcoded media content based on the plurality of calibrated scores and an exponential weighting function. The exponential weighting function exponentiates each of the calibrated scores by a respective weighting exponent and aggregates the exponentiated calibrated scores. The method includes selectively reencoding the transcoded media content based on the aggregated quality score.
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公开(公告)号：US20210112295A1
公开(公告)日：2021-04-15
申请号：US16613961
申请日：2017-12-12
Applicant: Google LLC
Inventor： Neil Birkbeck , Balineedu Adsumilli , Damien Kelly
IPC: H04N21/2662 , H04N21/81 , H04N21/233 , H04N21/234 , H04N21/4728
Abstract: Signals of an immersive multimedia item are jointly considered for optimizing the quality of experience for the immersive multimedia item. During encoding, portions of available bitrate are allocated to the signals (e.g., a video signal and an audio signal) according to the overall contribution of those signals to the immersive experience for the immersive multimedia item. For example, in the spatial dimension, multimedia signals are processed to determine spatial regions of the immersive multimedia item to render using greater bitrate allocations, such as based on locations of audio content of interest, video content of interest, or both. In another example, in the temporal dimension, multimedia signals are processed in time intervals to adjust allocations of bitrate between the signals based on the relative importance of such signals during those time intervals. Other techniques for bitrate optimizations for immersive multimedia streaming are also described herein.
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