公开(公告)号：US20220210432A1

公开(公告)日：2022-06-30

申请号：US17135972

申请日：2020-12-28

Applicant: ATI Technologies ULC

Inventor： Feng Pan ,  Crystal Yeong-Pian Sau ,  Wei Gao ,  Mingkai Shao ,  Dong Liu ,  Ihab M. A. Amer ,  Gabor Sines

IPC: H04N19/14 ,  H04N19/124 ,  H04N19/196 ,  H04N19/176

Abstract: A processing apparatus and video encoding method are provided which include receiving a portion of a video sequence and determining complexities for blocks of pixels of the portion of the video sequence. Quantization parameter values for corresponding blocks of pixels are selected based on complexities of the corresponding blocks and visually perceived coding artifacts of the corresponding blocks produced by the quantization parameter values. The blocks of pixels are encoded, using the selected quantization parameter values. The blocks of pixels are decoded and the portion of the video sequence is provided for display.

公开(公告)号：US20220210479A1

公开(公告)日：2022-06-30

申请号：US17139372

申请日：2020-12-31

Applicant: ATI Technologies ULC

Inventor： Wei Gao ,  Ihab Amer ,  Feng Pan ,  Mingkai Shao ,  Crystal Sau ,  Dong Liu ,  Gabor Sines ,  Yang Liu

IPC: H04N19/90 ,  H04N19/154

Abstract: Methods and apparatus provide cloud-based video encoding that generates encoded video data by one or more encoders in a cloud platform for a plurality of cloud encoding sessions. The methods and apparatus generate operational improvement tradeoff data in response to operational encoding metrics associated with the one or more encoders and change operational characteristics of the one or more encoders for at least one of the cloud encoding sessions based on the operational improvement tradeoff data.

公开(公告)号：US20220210429A1

公开(公告)日：2022-06-30

申请号：US17138812

申请日：2020-12-30

Applicant: ATI Technologies ULC

Inventor： Mehdi Saeedi ,  Sai Harshita Tupili ,  Yang Liu ,  Mingkai Shao ,  Gabor Sines

IPC: H04N19/136 ,  H04N19/172 ,  H04N19/103

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

公开(公告)号：US11490090B2

公开(公告)日：2022-11-01

申请号：US17138812

申请日：2020-12-30

Applicant: ATI Technologies ULC

Inventor： Mehdi Saeedi ,  Sai Harshita Tupili ,  Yang Liu ,  Mingkai Shao ,  Gabor Sines

IPC: H04N19/136 ,  H04N19/103 ,  H04N19/172

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

公开(公告)号：US20240348795A1

公开(公告)日：2024-10-17

申请号：US18439204

申请日：2024-02-12

Applicant: ATI Technologies ULC

Inventor： Sunil Gopal Koteyar ,  Mingkai Shao

IPC: H04N19/139 ,  G06N20/00 ,  G06T3/40 ,  G06V20/40 ,  H04N19/126 ,  H04N19/142 ,  H04N19/55

CPC classification number: H04N19/139 ,  G06N20/00 ,  G06T3/40 ,  G06V20/49 ,  H04N19/126 ,  H04N19/142 ,  H04N19/55

Abstract: Systems, apparatuses, and methods for performing machine learning content categorization leveraging video encoding pre-processing are disclosed. A system includes at least a motion vector unit and a machine learning (ML) engine. The motion vector unit pre-processes a frame to determine if there is temporal locality with previous frames. If the objects of the scene have not changed by a threshold amount, then the ML engine does not process the frame, saving computational resources that would typically be used. Otherwise, if there is a change of scene or other significant changes, then the ML engine is activated to process the frame. The ML engine can then generate a QP map and/or perform content categorization analysis on this frame and a subset of the other frames of the video sequence.

公开(公告)号：US20230095541A1

公开(公告)日：2023-03-30

申请号：US17488944

申请日：2021-09-29

Applicant: ATI Technologies ULC

Inventor： Sunil Gopal Koteyar ,  Mingkai Shao

IPC: H04N19/139 ,  H04N19/126 ,  G06K9/00 ,  H04N19/142 ,  G06T3/40 ,  H04N19/55 ,  G06N20/00

Abstract: Systems, apparatuses, and methods for performing machine learning content categorization leveraging video encoding pre-processing are disclosed. A system includes at least a motion vector unit and a machine learning (ML) engine. The motion vector unit pre-processes a frame to determine if there is temporal locality with previous frames. If the objects of the scene have not changed by a threshold amount, then the ML engine does not process the frame, saving computational resources that would typically be used. Otherwise, if there is a change of scene or other significant changes, then the ML engine is activated to process the frame. The ML engine can then generate a QP map and/or perform content categorization analysis on this frame and a subset of the other frames of the video sequence.

公开(公告)号：US11902532B2

公开(公告)日：2024-02-13

申请号：US17488944

申请日：2021-09-29

Applicant: ATI Technologies ULC

Inventor： Sunil Gopal Koteyar ,  Mingkai Shao

IPC: H04N19/139 ,  H04N19/126 ,  G06N20/00 ,  G06T3/40 ,  H04N19/55 ,  H04N19/142 ,  G06V20/40

CPC classification number: H04N19/139 ,  G06N20/00 ,  G06T3/40 ,  G06V20/49 ,  H04N19/126 ,  H04N19/142 ,  H04N19/55

Abstract: Systems, apparatuses, and methods for performing machine learning content categorization leveraging video encoding pre-processing are disclosed. A system includes at least a motion vector unit and a machine learning (ML) engine. The motion vector unit pre-processes a frame to determine if there is temporal locality with previous frames. If the objects of the scene have not changed by a threshold amount, then the ML engine does not process the frame, saving computational resources that would typically be used. Otherwise, if there is a change of scene or other significant changes, then the ML engine is activated to process the frame. The ML engine can then generate a QP map and/or perform content categorization analysis on this frame and a subset of the other frames of the video sequence.

公开(公告)号：US11568527B2

公开(公告)日：2023-01-31

申请号：US17031076

申请日：2020-09-24

Applicant: ATI TECHNOLOGIES ULC

Inventor： Feng Pan ,  Yang Liu ,  Crystal Sau ,  Wei Gao ,  Mingkai Shao ,  Dong Liu ,  Ihab Amer ,  Gabor Sines

IPC: G06T7/00 ,  H04N17/00 ,  H04N21/234 ,  G06V10/30 ,  G06V10/50 ,  G06V10/75

Abstract: Calculating, for each frame of a plurality of frames, a corresponding quality value; calculating, for each frame of the plurality of frames, based on one or more visual attributes of a frame, a weight for the corresponding quality value of the frame; calculating an aggregate quality value for the plurality of frames based on the weight and the corresponding quality value for each frame of the plurality of frames; and providing an assessment of the plurality of frames based on the aggregate quality value for the plurality of frames.

公开(公告)号：US11490127B2

公开(公告)日：2022-11-01

申请号：US17139372

申请日：2020-12-31

Applicant: ATI Technologies ULC

Inventor： Wei Gao ,  Ihab Amer ,  Feng Pan ,  Mingkai Shao ,  Crystal Sau ,  Dong Liu ,  Gabor Sines ,  Yang Liu

IPC: H04N19/90 ,  H04N19/154

Abstract: Methods and apparatus provide cloud-based video encoding that generates encoded video data by one or more encoders in a cloud platform for a plurality of cloud encoding sessions. The methods and apparatus generate operational improvement tradeoff data in response to operational encoding metrics associated with the one or more encoders and change operational characteristics of the one or more encoders for at least one of the cloud encoding sessions based on the operational improvement tradeoff data.