Disney Enterprises, Inc.

Anna M Wolak

Erika Varis Doggett

David T Nguyen

Erick Keyu Qi

Weichu Cui

Yingying Zhong

Nicholas McCarthy

Stephan Marcel Mandt

Penelope Daphne Tsatsoulis

Binghao Tang

Hailing Zhou

According to one implementation, a system includes a computing platform having a hardware processor and a system memory storing a software code including a trained neural network (NN). The hardware processor executes the software code to receive an input image including a pixel anomaly, identify, using the trained NN, one or more salient regions of the input image, and determine whether the pixel anomaly is located inside any of the one or more salient regions. The hardware processor further executes the software code to assign a first priority to the pixel anomaly when it is determined that the pixel anomaly is located inside any of the one or more salient regions, and to assign a second priority, lower than the first priority, to the pixel anomaly when it is determined that the pixel anomaly is not located inside any of the one or more salient regions.

Saliency prioritization for image processing

A system includes a hardware processor and a memory storing a software code including a predictive model. The hardware processor executes the software code to receive an input including an image having a pixel anomaly, and image data identifying the location of the pixel anomaly in the image. The software code uses the predictive model to extract a global feature map of a global image region of the image, the pixel anomaly being located within the global image region; to extract a local feature map of a local image region of the image, the pixel anomaly being located within the local image region and the local image region being smaller than the global image region; and to predict, based on the global feature map and the local feature map, a distraction level of the pixel anomaly within the image.

Automated prediction of pixel error noticeability

A process receives, with a processor, video content. Further, the process splices, with the processor, the video content into a plurality of video frames. In addition, the process splices, with the processor, at least one of the plurality of video frames into a plurality of image patches. Moreover, the process performs, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch. The process also compares, with the processor, the reconstructed image patch with the at least one of the plurality of image patches. Finally, the process determines, with the processor, a pixel error within the at least one of the plurality of image patches based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches.

Pixel error detection system

Growing community of inventors

Seattle, WA, United States of America

Anna M Wolak