Cinegy DANIEL2 Adobe Codec Pack v1.0.104 Win

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Info:
Adobe CC Plugin
Plugin for Adobe CC import, edit and professional video output in 8, 10, 12 or 16 bit color as well as accelerated H.264 / HEVC export.

Daniel2 Codec Summary
Sample title
Traditionally video codecs have been defined by chip makers and not by software developers. Daniel2 is different. It was designed by software developers that for decades had suffered implementing “industry standard” codecs such as MPEG2 or H.264 in software running on ordinary PC hardware, but these had not been created with PC hardware in mind. Daniel2 was designed to be a massively multi-threaded parallel processing software codec that runs fast on CPUs and even faster on GPUs with their thousands of parallel cores and ultra fast RAM.

The Daniel2 codec is acquisition and production codec meant to be used for recording from camera sources, editing and post-production as well as playout. Daniel2 is aiming for the same space in the production workflow as AVID’s DNxHR, Apple’s ProRes, JPEG2000 or Sony’s XAVC.

Application Areas
• Post-production – massively improved throughput and faster processing
• High density multi-channel HD, UHD/4K or 8K recording
• HFR Cinema, 8K and beyond broadcast acquisitions – perform ultra high-res HDR recording using commodity IT hardware
• High-end VR Production – ultra high-res 360 degree recording and playback
• Ultra high-speed recording – in HD, 4K, 8K, 16K or even higher custom resolutions
• Mobile 8K and/or multi-cam 4K editing on notebooks
• Ultra large scale imaging – much faster processing combined with variable compression quality ranging from lossy to lossless
• Medical imaging – visually lossless or mathematically lossless many times faster than JPEG2000
• Video over IP for broadcast, film, security, telco and many other applications
• High-end KVM over IP with very low latency and better fidelity than H.264 or H.265
• Display walls and tiled multi-screen / multi-projector presentations
• AI & Computer Vision – vastly improved throughput and lower memory consumption for faster deep learning and computer vision
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