a5202bc968
This improves accuracy for the bessel function at large arguments, and this in turn should improve the quality of the Kaiser window. It also improves the performance of the bessel function and hence build_filter by ~ 20%. Details are given below. Algorithm: taken from the Boost project, who have done a detailed investigation of the accuracy of their method, as compared with e.g the GNU Scientific Library (GSL): http://www.boost.org/doc/libs/1_52_0/libs/math/doc/sf_and_dist/html/math_toolkit/special/bessel/mbessel.html. Boost source code (also cited and licensed in the code): https://searchcode.com/codesearch/view/14918379/. Accuracy: sample values may be obtained as follows. i0 denotes the old bessel code, i0_boost the approach here, and i0_real an arbitrary precision result (truncated) from Wolfram Alpha: type "bessel i0(6.0)" to reproduce. These are evaluation points that occur for the default kaiser_beta = 9. Some illustrations: bessel(8.0) i0 (8.000000) = 427.564115721804739678191254 i0_boost(8.000000) = 427.564115721804796521610115 i0_real (8.000000) = 427.564115721804785177396791 bessel(6.0) i0 (6.000000) = 67.234406976477956163762428 i0_boost(6.000000) = 67.234406976477970374617144 i0_real (6.000000) = 67.234406976477975326188025 Reason for accuracy: Main accuracy benefits come at larger bessel arguments, where the Taylor-Maclaurin method is not that good: 23+ iterations (at large arguments, since the series is about 0) can cause significant floating point error accumulation. Benchmarks: Obtained on x86-64, Haswell, GNU/Linux via a loop calling build_filter 1000 times: test: fate-swr-resample-dblp-44100-2626 new: 995894468 decicycles in build_filter(loop 1000), 256 runs, 0 skips 1029719302 decicycles in build_filter(loop 1000), 512 runs, 0 skips 984101131 decicycles in build_filter(loop 1000), 1024 runs, 0 skips old: 1250020763 decicycles in build_filter(loop 1000), 256 runs, 0 skips 1246353282 decicycles in build_filter(loop 1000), 512 runs, 0 skips 1220017565 decicycles in build_filter(loop 1000), 1024 runs, 0 skips A further ~ 5% may be squeezed by enabling -ftree-vectorize. However, this is a separate issue from this patch. Reviewed-by: Michael Niedermayer <michael@niedermayer.cc> Signed-off-by: Ganesh Ajjanagadde <gajjanagadde@gmail.com>
FFmpeg README
FFmpeg is a collection of libraries and tools to process multimedia content such as audio, video, subtitles and related metadata.
Libraries
libavcodecprovides implementation of a wider range of codecs.libavformatimplements streaming protocols, container formats and basic I/O access.libavutilincludes hashers, decompressors and miscellaneous utility functions.libavfilterprovides a mean to alter decoded Audio and Video through chain of filters.libavdeviceprovides an abstraction to access capture and playback devices.libswresampleimplements audio mixing and resampling routines.libswscaleimplements color conversion and scaling routines.
Tools
- ffmpeg is a command line toolbox to manipulate, convert and stream multimedia content.
- ffplay is a minimalistic multimedia player.
- ffprobe is a simple analysis tool to inspect multimedia content.
- ffserver is a multimedia streaming server for live broadcasts.
- Additional small tools such as
aviocat,ismindexandqt-faststart.
Documentation
The offline documentation is available in the doc/ directory.
The online documentation is available in the main website and in the wiki.
Examples
Coding examples are available in the doc/examples directory.
License
FFmpeg codebase is mainly LGPL-licensed with optional components licensed under GPL. Please refer to the LICENSE file for detailed information.
Contributing
Patches should be submitted to the ffmpeg-devel mailing list using
git format-patch or git send-email. Github pull requests should be
avoided because they are not part of our review process. Few developers
follow pull requests so they will likely be ignored.