- Number
- 11080835
- Published
- 2021-08-03
- Filed
- 2019-01-09
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Doggett; Erika, Wolak; Anna, Tsatsoulis; Penelope Daphne, McCarthy; Nicholas, Mandt; Stephan
- CPC
- G06V20/41; G06V10/82; G06V20/40; G06T7/0002; G06V10/764
- Verdict
- Set aside pixel error QC tool, media production
- Source
- Google Patents · FreePatentsOnline
Abstract
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.
Background
BACKGROUND 1. Field (1) This disclosure generally relates to the field of video production. 2. General Background (2) A video production pipeline may involve various stages, from start to finish, for producing video content (e.g., movies, television shows, video games, etc.). During video production, errors may be introduced into one or more of the video frames of the video content that diminish the quality of the final video content product. Such errors may result from image capture equipment not properly being maintained, image capture equipment malfunctions, or artifacts being inserted into the video during the rendering process. Using humans to perform error checking on a frame-by-frame basis is labor-intensive, often involving multiple workers reviewing the same content in the video production pipeline. Moreover, conventional computerized configurations have attempted to use computer vision technology (i.e., attempting to understand imagery in a manner similar to humans), but such attempts have typically led to either not catching enough errors or detecting too many false positives when performing error detection. Therefore, previous approaches do not efficiently and accurately detect errors in a video production pipeline. SUMMARY (3) In one aspect, a computer program product comprises a non-transitory computer readable storage device having a computer readable program stored thereon. The computer readable program when executed on a computer causes the computer to receiv
Claims
1. A computer program product comprising a non-transitory computer readable storage device having a computer readable program stored thereon, wherein the computer readable program when executed on a computer causes the computer to: receive, with a processor, video content; splice, with the processor, the video content into a plurality of video frames; splice, with the processor, at least one of the plurality of video frames into a plurality of image patches; perform, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch; compare, with the processor, the reconstructed image patch with the at least one of the plurality of image patches; generate, with the processor, an error score based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches; determine, with the processor, whether the error score is an outlier based on a comparison of the error score with a plurality of error scores corresponding to a distribution of error scores for the plurality of image patches; and determine, with the processor, whether a pixel error is present within the at least one of the plurality of image patches when the error score is determined to be the outlier. ||
9. A method comprising: receiving, with a processor, video content; splicing, with the processor, the video content into a plurality of video frames; splicing, with the processor, at least one of the plurality of video frames into a plurality of image patches; performing, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch; comparing, with the processor, the reconstructed image patch with the at least one of the plurality of image patches; generating, with the processor, an error score based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches; determining, with the processor, whether the error score is an outlier based on a comparison of the error score with a plurality of error scores corresponding to a distribution of error scores for the plurality of image patches; and determining, with the processor, whether a pixel error is present within the at least one of the plurality of image patches when the error score is determined to be the outlier. ||
16. An apparatus comprising: a processor that receives video content, splices the video content into a plurality of video frames, splices at least one of the plurality of video frames into a plurality of image patches, compares a reconstructed image patch with the at least one of the plurality of image patches, generates an error score based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches, determines whether the error score is an outlier based on a comparison of the error score with a plurality of error scores corresponding to a distribution of error scores for the plurality of image patches, and determines whether a pixel error is present within the at least one of the plurality of image patches when the error score is determined to be the outlier; and a neural network that performs an image reconstruction of the at least one of the plurality of image patches to generate the reconstructed image patch.