shot-boundary-detection

Description

Survey and implementations of multiple shot-boundary detection techniques including hard-cuts, fades, and neural-net methods.

URL

• Visit resource

Related resources

• MDLSeg: Parameter-Free Video Scene Segmentation via Minimum Description LengthAcademic paper proposing a parameter-free scene-segmentation method claimed to outperform PySceneDetect.
• SceneSegmentation-SCRLOfficial PyTorch implementation of CVPR 2022 Scene Consistency Representation Learning for movie-level scene segmentation (MovieNet/SceneSe…
• KSQI: Knowledge-driven Streaming Quality IndexKnowledge-driven QoE model for adaptive streaming video with implementations of multiple objective QoE models, companion to arXiv:1911.0794…
• Evaluating Video Quality Metrics for Neural and Traditional Codecs (4K/UHD-1)Academic comparison of VMAF, AVQBits, and FasterVQA no-reference quality metrics on 4K content.
• RobustVideoMattingReal-time human video matting via recurrent neural network requiring no green screen, achieving 4K 76FPS on GTX 1080 Ti, companion to arXiv…
• Bitrate Ladder Construction via Transfer Learning and Spatio-Temporal FeaturesML-based bitrate ladder optimization achieving 94.1% complexity reduction at only 1.71% BD-rate cost using transfer learning.
• VMAF-torchPyTorch reimplementation of VMAF metric, GPU/gradient-friendly for use in learned codec optimization loops.
• CompressedVQA: Full/No-Reference VQA for Compressed UGC VideoICME 2021 grand challenge-winning full-reference and no-reference video quality models for compressed user-generated content.
• 2BiVQA: Double Bi-LSTM No-Reference Video Quality AssessmentNo-reference VQA model for UGC video using double Bi-LSTM, companion code to arXiv:2208.14774.
• nr-vqa-consumervideoNo-reference video quality metric tuned for consumer video capture artifacts (sensor noise, motion blur, shake) rather than compression art…