Video Artifact Detection via Motion Vector Cost Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy methods for video quality control in file-based workflows are inadequate, as they are inconsistent, subjective, and difficult to scale, leading to high false negatives and inability to handle new types of errors, especially in a globally accessible media workflow where consumer expectations for HD video quality are increasing.
Innovation Solution
A top-down approach for error detection in video sequences based on generic spatiotemporal characteristics, using motion vector costs, filter coefficients, and variable thresholds to identify artifacts, with scene change and exception handling, enabling robust detection of various errors regardless of their nature or location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a bottom-up approach is used to detect specific types of errors independently, then false positives are reduced, but false negatives increase and the system cannot handle new types of errors
Solution Approach 1:
The patent inverts the traditional bottom-up approach by implementing a top-down method. Instead of detecting specific error types independently from the ground up, the system starts with a global motion analysis that can identify various artifacts (block artifacts, motion compensation errors, blending artifacts) through overall motion patterns. This inversion allows the system to maintain low false positives while gaining the ability to detect multiple error types including new ones.
Solution Approach 2:
The patent creates a universal error detection mechanism that can handle multiple types of video artifacts through a single top-down motion analysis framework. The system uses motion vector cost analysis and spatiotemporal filtering to detect block artifacts, motion compensation errors, blending artifacts, and other errors with a unified approach, making the system adaptable to new error types without requiring separate detection mechanisms for each.
2Reliability
If manual quality control methods are used in file-based workflows, then quality checks can be performed, but the process becomes inconsistent, subjective, and difficult to scale
Solution Approach 1:
The patent replaces manual mechanical quality control processes with an automated computational system. The top-down motion analysis methodology uses algorithms to calculate motion vector costs, apply spatiotemporal filtering, and detect artifacts automatically, eliminating subjectivity and inconsistency inherent in manual review. This substitution enables the system to scale efficiently while maintaining reliable, consistent quality control across large volumes of video content.
Solution Approach 2:
The patent implements a self-service quality control system where the video content itself provides the information needed for detection. The system uses motion vectors and pixel data already present in the video stream to automatically identify artifacts without requiring external manual intervention. The algorithm serves itself by using the video's own structural information (motion patterns, spatiotemporal characteristics) to detect quality issues, enabling scalable automated operation.
3Adaptability or versatility
If complex operations are performed on media files for compression and formatting, then flexibility and output options increase, but errors and quality degradation are introduced
Solution Approach 1:
The patent applies preliminary action by performing quality detection early in the file-based workflow, before final delivery. The top-down motion analysis is executed on the processed video content to identify artifacts introduced during compression, formatting, and other complex operations. By detecting issues preliminarily, the system enables corrective actions before quality degradation reaches the consumer, maintaining video quality integrity despite the flexibility-providing complex operations.
Data Source
AI summary
A system and method for detecting artifacts in a video having a sequence of pictures is described. The method comprises the steps of calculating a motion vector cost and a variable threshold for each of the pictures. In case, the motion vector cost for the picture is greater than the variable threshold of the picture, then a first scene change profile and a second scene change profile for the picture is computed and analyzed for determining the character of the variation in the motion vector cost. An artifact metric for the picture is also calculated and the artifact metric for the picture is compared with a programmable artifact reporting threshold for ascertaining if an artifact is present in the video. The described approach is generic in nature and takes into account various exceptions that may be present within a picture that may be similar in character as an artifact.