Pixel-Level Artifact Detection Using Multi-Scale CNN Features

The patent replaces the mechanical human inspection system with an automated machine learning system comprising convolutional neural networks and artifact detection models. This substitution maintains detection accuracy through trained algorithms while dramatically increasing processing speed and productivity by automatically analyzing video frames without human intervention.

The artifact detection system performs self-service by automatically detecting, localizing, and classifying artifacts in video content without requiring manual inspection. The machine learning model independently processes video frames, identifies artifacts based on learned patterns, and generates detection results, eliminating the need for human operators while maintaining consistent accuracy.

The patent segments the high-resolution video content into individual frames and further segments artifact detection into multiple processing stages using different convolutional blocks. The first convolutional block processes initial features, the second and third blocks refine bottleneck features, and the fourth and fifth blocks generate final artifact detections. This segmentation makes detecting artifacts in high-resolution content manageable by breaking down the complex task into smaller, parallelizable operations.

The patent introduces an additional processing dimension by implementing a multi-block convolutional architecture that processes video data through multiple transformation stages. Instead of attempting to detect artifacts directly in high-resolution space, the system transforms the data through multiple convolutional blocks that operate in feature space, effectively adding a dimensional layer to the detection process that simplifies the identification of artifacts regardless of input resolution.