Imax Corporation

Ssimwave Inc.

Kaiwen Ye

Zhou Wang

Kai Zeng

Jiheng Wang

Hojatollah Yeganeh

Ahmed Badr

Abdul Rehman

No-reference (NR) quality assessment (VQA) of a test visual media input encoding media content is provided. The test video visual media input is decomposed into multiple-channel representations. Domain knowledge is obtained by performing content analysis, distortion analysis, human visual system (HVS) modeling, and/or viewing device analysis. The multiple-channel representations are passed into deep neural networks (DNNs) producing DNN outputs. The DNN outputs are combined using domain knowledge to produce an overall quality score of the test visual media input.

No-reference visual media assessment combining deep neural networks and models of human visual system and video content/distortion analysis

Frames of a video stream collected at first and second points along a video delivery chain are buffered into first and second buffers, respectively, the second point being downstream the video delivery chain from the first point. A control group is identified as a subset of frames of the second buffer. Correlations of extracted features of the control group to extracted features of successive windows of frames of the first buffer are computed, the extracted features being based on spatial information and temporal information of the video stream. A delay between the video stream collected at the first point and the video stream collected at the second point is identified according to a maximum correlation of the correlations.

Real-time latency measurement of video streams

Growing community of inventors

North York, Canada

Kaiwen Ye