BACKGROUND

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.

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

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.

