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.

Vcodec h264