University of Texas System

Other

Netflix Inc.

Todd Goodall

Alan C Bovik

Zhi Li

Anne Aaron

Jeremy Joachim

Michael Glombicki

Jason Joachim

Shai Bernstein

In various embodiments, a quality trainer trains a model that computes a value for a perceptual video quality metric for encoded video content. During a pre-training phase, the quality trainer partitions baseline values for metrics that describe baseline encoded video content into partitions based on genre. The quality trainer then performs cross-validation operations on the partitions to optimize hyperparameters associated with the model. Subsequently, during a training phase, the quality trainer performs training operations on the model that includes the optimized hyperparameters based on the baseline values for the metrics to generate a trained model. The trained model accurately tracks the video quality for the baseline encoded video content. Further, because the cross-validation operations minimize any potential overfitting, the trained model accurately and consistently predicts perceived video quality for non-baseline encoded video content across a wide range of genres.

Techniques for robustly predicting perceptual video quality

A method, system and computer program product for measuring non-uniformity noise produced in images or videos (e.g., infrared images or videos). Images or videos, such as infrared images or videos, are captured. A model of scene statistics (statistical model of pictures, images or videos representative of pictures, images or videos, respectively, that are captured of the physical world) is utilized to measure the non-uniformity noise in the captured images or videos by exploiting exhibited characteristics for non-uniformity noise in the captured images or videos. A number signifying a magnitude of non-uniformity for each image or video frame is then generated. In this manner, non-uniformity noise produced in images or videos is measured.

Measurement of non-uniformity noise

A method, system and computer program product for classifying an image or video. An image or video to be classified is received. Scene statistics (statistical model of pictures, images or videos representative of pictures, images or videos, respectively, that are captured of the physical world) of the image or video are captured. A model (a statistical model that describes a set of probability distributions) of the image or video is then created using the captured scene statistics. A comparison between the model of the image or video with two other models of images or videos is performed, such as a model of visible light images or videos and a model of infrared images or videos. The received image or video is then classified (e.g., classified as corresponding to a visible light image) based on the comparison.

Classifying images and videos

A system to digitally scan an object in three dimensions is disclosed. The system includes a server system, a memory system, a website and a scanner that scans an object and transfers any number of scanner data to the memory system, the scanner data resides on the digitally scan an object in three dimensions non-transitory storage media and includes any number of object dimensions such as length, width and height of the object, a three-dimensional object file and any number of raw video or any number of images of the object. The system includes a corresponding method for digitally scanning an object in three dimensions and a non-transitory computer storage media having instructions stored thereon which, when executed, execute the method for digitally scanning an object in three dimensions.

Growing community of inventors

Austin, TX, United States of America

Todd Goodall