Automated prediction of pixel error noticeability

Number: 11797649
Published: 2023-10-24
Filed: 2022-10-03
Assignee: Disney Enterprises, Inc.
Inventors: Doggett; Erika Varis et al.
CPC: G06F18/2148; G06F18/41; G06T7/0002; G06T7/0004; G06V10/25; G06V10/82; G06V40/20
Verdict: Set aside generic image QC/anomaly detection
Source: Google Patents · FreePatentsOnline

Abstract

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.

Background

BACKGROUND (1) Pixel errors in images occur with regularity but can be difficult and costly to correct. For example, pixel anomalies in video frames can be introduced by many different processes within a video production pipeline. A final quality procedure for correcting such errors is typically done before the video undergoes final release, and in the conventional art that process is usually performed by human inspectors. Due to its reliance on human participation, pixel error correction is expensive and time consuming. (2) However, not all pixel errors require correction. For example, depending on its position within an image, its visual impact relative to other features in its local environment within the image, as well as the visual qualities of the image as a whole, some pixel anomalies may be highly distracting, while others may be less so, and still others may reasonably be disregarded without significantly affecting an intended esthetic of the image. That is to say, not all pixel errors are of equal importance. Accordingly, there is a need in the art for an automated approach to predicting the noticeability of pixel errors in an image. SUMMARY (3) There are provided systems and methods for performing automated prediction of pixel error noticeability, substantially as shown in and described in connection with at least one of the figures, and as set forth more completely in the claims.

Claims

1. A system comprising: a hardware processor; and a system memory storing a software code including a predictive model; the hardware processor configured to execute the software code to: receive an image having a pixel anomaly; receive image data providing a location of the pixel anomaly in the image, and further providing a parameter of the pixel anomaly, the parameter including at least one of a color value, a contrast, or a distance from a center of the image; extract, using the predictive model, a feature map of a region of the image, the pixel anomaly being located within the region; predict, using the predictive model and based on the feature map and the parameter, a distraction level of the pixel anomaly in the image. || 10. A method for use by a system having a hardware processor and a system memory storing a software code including a predictive model, the method comprising: receiving, by the software code executed by the hardware processor, an image having a pixel anomaly; receiving, by the software code executed by the hardware processor, image data providing a location of the pixel anomaly in the image, and further providing a parameter of the pixel anomaly, the parameter including at least one of a color value, a contrast, or a distance from a center of the image; extracting, by the software code executed by the hardware processor and using the predictive model, a feature map of a region of the image, the pixel anomaly being located within the region; predicting, by the software code executed by the hardware processor and using the predictive model, based on the feature map and the parameter, a distraction level of the pixel anomaly in the image.