Ati Technologies Ulc

Mingkai Shao

Gabor Sines

Yang Liu

Feng Pan

Wei Gao

Dong Liu

Ihab Amer

Crystal Sau

Mehdi Saeedi

Ihab M A Amer

Crystal Yeong-Pian Sau

Sunil Gopal Koteyar

Sai Harshita Tupili

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.

Quantization parameter map for video encoding with constant perceptual quality

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.

Video encoding optimization for machine learning content categorization

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.

Video quality assessment using aggregated quality values

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.

Method and apparatus for providing cloud-based video encoding

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.

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Markham, Canada

Mingkai Shao