We are pleased to announce the release of PyNvVideoCodec 2.1, the latest version of our high-performance, GPU-accelerated Python library for video decoding, encoding, and transcoding , all with the simplicity and flexibility of Python.
This release brings in deeper insights into video and enhanced usability, making it easier to build, optimize, and scale video workflows for AI.
New Features in PyNvVideoCodec 2.1
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Video insights through decode statistics: Access to low-level decode statistics such as QP, coding units, and motion vectors for deeper insights into video — useful for smarter video curation and training.
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Jupyter notebook tutorials: Interactive examples showcasing SimpleDecoder for frame sampling and a real-world object detection pipeline where ThreadedDecoder overlaps decoding with inference for higher throughput.
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Enhanced sample applications: Simplified samples with improved documentation to help you prototype and experiment faster.
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Enhanced documentation: A comprehensive programming guide and API reference with practical code snippets to quickly understand the library.
Refer for more details: Getting started with PyNvVideoCodec