- 10 Jan, 2014 - 4 commits
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Jingning Han authored
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Jingning Han authored
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Deb Mukherjee authored
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Deb Mukherjee authored
Cleanups on frame refresh flags and external overrides. Change-Id: Ia6a56fe1bde906b1dc3fcbf4ef1c7b207cd2df2d
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- 09 Jan, 2014 - 18 commits
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Johann authored
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Yaowu Xu authored
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Dmitry Kovalev authored
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Yaowu Xu authored
1. Made speed choices to be progressive 2. Adjusted rt speed settings to achieve better speed/quality Overall, rt-5 gained 2.5% in compression/quality, encoding time of 720p niklas clip goes from 137,052ms to 121,874ms Change-Id: Ia6e7e1e15225395a868a2f1059c3db8e266e1600
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Jingning Han authored
This commit further optimizes SSE2 operations in the second 1-D inverse 16x16 DCT, with (<10) non-zero coefficients. The average runtime of this module goes down from 779 cycles -> 725 cycles. Change-Id: Iac31b123640d9b1e8f906e770702936b71f0ba7f
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Yunqing Wang authored
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levytamar82 authored
Optimizing all SSSE3 assembly for convolution: 1. vp9_filter_block1d4_h8_sse2 2. vp9_filter_block1d8_h8_sse2 3. vp9_filter_block1d16_h8_sse2 4. vp9_filter_block1d4_v8_sse2 5. vp9_filter_block1d8_v8_sse2 6. vp9_filter_block1d16_v8_sse2 my optimization include: -processing 2x8 elements in one 128 bit register instead of processing 8 elements in one 128 bit register. -removing unecessary loads. This optimization gives between 2.4% user level gain for 480p input and 1.6% user level gain for 720p. This Optimization done only for 64bit. Change-Id: Icb586dc0c938b56699864fcee6c52fd43b36b969
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Dmitry Kovalev authored
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Dmitry Kovalev authored
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Johann authored
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James Yu authored
Add bilinearpredict_neon_intrinsics.c - vp8_bilinear_predict4x4_neon - vp8_bilinear_predict8x4_neon - vp8_bilinear_predict8x8_neon - vp8_bilinear_predict16x16_neon Change-Id: I33dfa502881219841b442dda32b73220e51b716b Signed-off-by:
James Yu <james.yu@linaro.org>
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Paul Wilkins authored
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Johann authored
On Windows this fails with: error C2440: 'initializing': cannot convert from int_mv to uint32_t Change-Id: I51630efd0e83a0ce620c91aa7859dd6fc1572e99
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Dmitry Kovalev authored
Change-Id: If7d3958653104f3e170853e931f8489de3ecf3cc
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Dmitry Kovalev authored
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Johann authored
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Dmitry Kovalev authored
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Dmitry Kovalev authored
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- 08 Jan, 2014 - 18 commits
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Johann authored
Move the code outside the conditions. The test sources themselves are also required for Visual Studio. Change-Id: Id5e93ebc7369e1807eba0b9dc4f7d0f18033d794
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Jingning Han authored
This commit is the first patch optimizing SSE2 implementation of inverse 16x16 DCT with <10 non-zero coefficients. It focused on the first 1-D (row) transformation. It exploits the fact that only top-left 4x4 block contains non-zero coefficients, in a 2-D inverse 16x16 DCT with <10 coeffients. The average runtime of idct16x16_10 unit is reduced from 883 cycles -> 779 cycles (12% faster). For pedestrian_area_1080p 300 frames at 4000 kbps, the speed 2 runtime goes down from 310651 ms -> 305910 ms. The decoding speed goes up from 80.37 fps -> 80.87 fps. Change-Id: Ic6f3ac5a637a76c07ba73ddaafe318a699fea645
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Dmitry Kovalev authored
Change-Id: Ib1d9628d2b538b6dc27b0db1fa7f40f70ff2072f
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Dmitry Kovalev authored
Change-Id: I0c42a729038d0f4cb7bc07f587d066fcb1dfe9d9
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Alex Converse authored
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hkuang authored
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Dmitry Kovalev authored
Change-Id: I6cdd670d66288dbd66228f38bba6b30502d25362
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Dmitry Kovalev authored
Change-Id: I54513dc3b3321e0c0bb6b15ea5c34085ed80b4a4
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Dmitry Kovalev authored
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Alex Converse authored
For systems without __builtin_clz() or _BitScanReverse(), taken from libwep Change-Id: Iead257efc1772c466c79e1dc0356ed571d38d43e
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hkuang authored
More intra optimizations will be added. Change-Id: I33ae8d93f6002bf7b64cc2669602d9e6bfa5a6e8
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Yunqing Wang authored
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Johann authored
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Alex Converse authored
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levytamar82 authored
Optimizing the variance functions: vp9_variance16x16, vp9_variance32x32, vp9_variance64x64, vp9_variance32x16, vp9_variance64x32, vp9_mse16x16 by migrating to AVX2 some of the functions were optimized by processing 32 elements instead of 16. some of the functions were optimized by processing 2 loop strides of 16 elements in a single 256 bit register This optimization gives between 2.4% - 2.7% user level performance gain and 42% function level gain. Change-Id: I265ae08a2b0196057a224a86450153ef3aebd85d
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Alex Converse authored
Change-Id: I44eb44eb3f36c05d916ef140ef42cc84f72f99ec
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Jingning Han authored
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Johann authored
Change-Id: I28e9cf9347acd7279df3b841863a248479633265
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