Vmware, Inc.

Xuwen Yu

Hari Sivaraman

Uday Pundalik Kurkure

Lan Vu

Aalap Desai

Rajit S Kambo

Xiaojun Liang

Aiguo Dong

A system and method for assessing video quality of a video-based application trains a neural network using training data of video samples and assesses video of the video-based application using the neural network to generate the subjective video quality information of the video-based application. Data augmentation is performed on video data, which is labeled with at least one subjective quality level, to generate the training data of video samples.

System and method for machine learning based video quality assessment

A system and method for assessing video quality of a video-based application inserts frame identifiers (IDs) into video content from the video-based application and recognizes the frame IDs from the video content using a text recognition neural network. Based on recognized frame IDs, a frame per second (FPS) metric of the video content is calculated. Based on the FPS metric of the video content, objective video quality of the video-based application is assessed.

Performance of server that is managing a group of servers, such as in a cloud, is analyzed by identifying patterns of threads executed in the server. A command is issued, repeatedly over a time period, to an operating system of the server to return a resource consumption value representative of the amount of computing resources consumed by one or more threads. The returned resource consumption values are stored and used to generate points on a graph which depicts the amount of computing resources consumed by the one or more threads over the time period and provide task breakdowns and latency information for each of the tasks.

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Palo Alto, CA, United States of America

Xuwen Yu