I started this section with this clarification because there's news in the wind that NVIDIA will be deprecating mobile Kepler GPUs in their mainline driver, relegating them to Legacy support status. All OpenCL-based filters: All NVENC-capable GPUs supported by both the mainline NVIDIA driver and the CUDA SDK implement OpenCL support. It's dependency, as stated above, is the ffnvcodec package of headers. yadif_cuda: This is a deinterlacer, implemented in CUDA. Use this filter in place of scale_cuda wherever possible. configure at compile time when building FFmpeg from source. It's primary dependency is the CUDA SDK, and it must be explicitly enabled by passing -enable-libnpp, -enable-cuda-nvcc and -enable-nonfree flags to. scale_npp: This is a scaling filter implemented in NVIDIA's Performance Primitives. In production, it may be wise to deprecate this filter in favor of scale_npp as it has a very limited set of options. When the ffnvcodec headers are present, the respective filters dependent on it (scale_cuda and yadif_cuda) will be automatically enabled. It's dependency is the ffnvcodec project, headers needed to also enable the NVENC-based encoders. scale_cuda: This is a scaling filter analogous to the generic scale filter, implemented in CUDA. For NVIDIA, the following filters can take advantage of hardware-acceleration: Hardware-accelerated filters: Filters that perform duties such as scaling and post-processing (deinterlacing, etc) are available in FFmpeg, and some implementations are hardware-accelerated. See this answer on how to tune them, and any limitations you may run into depending on the generation of hardware you're on.Ģ. Hardware-accelerated encoders: In the case of NVIDIA, NVENC is supported and implemented via the h264_nvenc and the hevc_nvenc wrappers. When it comes to hardware acceleration in FFmpeg, you can expect the following implementations by type:ġ.
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