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: Identifying visual artifacts or "cracks" in the digital signal during high-speed encoding. Optimization
use hierarchical convolutional features to distinguish between actual structural cracks and irrelevant surface noise. 2. Video Transcoding and Compression
: Recent advancements involve using deep semantic segmentation and encoder-decoder architectures (like EfficientNet ) to identify and quantify surface cracks from image data. Segment Any Crack : Research has adapted models like the Segment Anything Model (SAM)
: Using deep learning to intelligently decide which parts of a frame require more data (bitrate) based on detected objects or textures.
While less common, the intersection of these topics involves using machine vision (Mv) to analyze video streams during the transcoding process. This is often used for: Quality Control
