The x264/x265 presets are all tailored around the metric of encoding speed and have only a non-linear correlation to quality or bitrate. I have to remind myself that there is a very good reason the presets are called faster-fast-medium-slow-slower rather than bigger-big-medium-small-smaller or bester-best-medium-worse-worser. Booth are encoded with 6 Mbit average bitrate fot the ffmpeg version, the -crf option was used and for the AME version, the CBR option. In CRF mode, a faster preset may require a higher bitrate to achieve the same CRF, or in bitrate mode, a faster preset may give you a lower quality for the same bitrate, but the only thing you can guarantee is that slower preset will be slower to encode and visa versa. How to use CUDA GPU hardware encoding with ffmpeg to encode h264 and h264 HEVC movies in high quality and highspeed with our optimized parameter settings. This is because it creates a more complex encode using more of the available features of the codec, which uses more memory and CPU.Īs you have identified, there is a correlation between the holy trinity of speed-quality-bitrate, but it is not a hard rule or linear scale. The only thing you can be sure of is that a slower preset will be slower to encode. What's the benefit of the slow preset if the videos look almost identical? The bitrate was higher on a the slow preset version. Here $N$ is set of agents at each iteration which are not pruned.I encoded the same video using crf of 20 and medium preset also with crf of 20 and slow preset. To invoke it, a command of the form is needed: ffmpeg -i in.mp4 -c:v h264qsv -globalquality 10 -lookahead 1 out.mp4. So I will try to setup the problem here as to avoid reading the complete section (personalized setting).Īssume there are $k$ agents that are learning a labelling function $f^))$ samples if their error is lower than $3\epsilon/4$. Media SDK's ICQ and LAICQ are the best match for this class of algorithm.
I am reading the paper "Collaborative PAC Learning" by Blum et al. Naren Asks: Generalization error bound in case of collaborative learning
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