Ssimwave Inc.

Imax Corporation

Ahmed Badr

Zhou Wang

Kai Zeng

Ian Wormsbecker

Abdul Rehman

Jiheng Wang

Hojatollah Yeganeh

Peter Olijnyk

Christopher Vytautas Olekas

Waqas Ahmed

Christopher Kulbakas

Kalyan Goswami

Kaiwen Ye

Joshua Vijay Mohan

Vladimir Sireaev

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

Correlated quality-of-experience (QoE) and latency measures are generated at a plurality of monitoring points along a multimedia delivery chain including multiple video operations. At each of the plurality of monitoring points, an absolute QoE measure defined on a human perceptual quality scale for media content is computed, and one or more of content extraction or feature extraction on the media content are performed. To a common middleware from each of the plurality of monitoring points, the respective QoE measure and results of the one or more of content extraction or feature extraction are transmitted. Computing and updating an absolute QoE measure for each of the plurality of monitoring points is performed. Computing and updating latencies between multiple monitoring points at the middleware using the results from each of the plurality of monitoring points is also performed.

Unified end-to-end quality and latency measurement, optimization and management in multimedia communications

Detection of scaling of compressed videos or images is provided. A frequency domain transformation is applied along both horizontal and vertical directions of input video or images to generate frequency domain data. Statistics in the frequency domain data are computed for each of the horizontal and vertical directions to extract features. The features are modeled to scores along each of the horizontal and vertical directions. An original resolution of the input video or images in the horizontal and vertical directions is identified according to the scores.

Scaling factor detection for compressed images and videos

Candidate encodes of a source video are produced using a plurality of encoding configurations. Quality of experience (QoE) scores for each of the candidate encodes are computed. The QoE scores of the plurality of candidate encodes are compared to determine a lowest bitrate encode that achieve a target QoE score. The lowest bitrate encode that achieve the target QoE score as an optimized output video is selected.

Perceptual quality assessment metric based content and viewer aware encoding

Classifying video for encoding optimization may include computing a content complexity score of a video, the content complexity score indicating a measure of how detailed the video is in terms of spatial and temporal information, categorizing the video into one of a plurality of buckets according to the content complexity score, each bucket representing a category of video content having a different range of content complexity scores and being associated with a ladder specific to the range, and encoding the video according to the ladder of the one of the plurality of buckets into which the video is categorized.

Video encoding complexity measure system

Real-time latency of audio/video streams is identified. Signatures of a reference audio/video stream and signatures of a test audio/video stream are buffered. A needle is constructed as a vector including a set of signatures of the reference audio/video stream. Correlations of the needle to successive vectors of sets of signatures of the test audio/video stream are computed using a correlation function that calculates relatedness of the needle vector to each of the successive vectors of the test audio/video stream. A synchronization offset is identified between the test stream and the reference stream according to a maximum correlation point of the correlations of the needle to the successive sets of signatures of the test audio/video stream. The reference audio/video stream and the test audio/video stream are aligned according to the synchronization offset.

Distributed measurement of latency and synchronization delay between audio/video streams

Banding effects in an image or video are assessed. The image or each frame of a video is decomposed into luminance or color channels. Signal activity is computed at each spatial location in each channel. A significance of the signal activity at each spatial location is determined by comparing each element of the signal activity with a significance threshold. Banding pixels are detected as those pixels of the image or frame having significant signal activity at their respective spatial locations and having non-significant signal activity of at least a minimum threshold percentage of neighboring pixels to the respective spatial locations.

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Waterloo, Canada

Ahmed Badr