Adds an experiment to use a weighted prediction of two INTER
predictors, where the weight is one of (1/4, 3/4), (3/8, 5/8),
(1/2, 1/2), (5/8, 3/8) or (3/4, 1/4), and is chosen implicitly
based on consistency of the predictors to the already
reconstructed pixels to the top and left of the current macroblock
or superblock.
Currently the weighting is not applied to SPLITMV modes, which
default to the usual (1/2, 1/2) weighting. However the code is in
place controlled by a macro. The same weighting is used for Y and
UV components, where the weight is derived from analyzing the Y
component only.
Results (over compound inter-intra experiment)
derf: +0.18%
yt: +0.34%
hd: +0.49%
stdhd: +0.23%
The experiment suggests bigger benefit for explicitly signaled weights.
Change-Id: I5438539ff4485c5752874cd1eb078ff14bf5235a
These are mostly just for experimental purposes. I saw small gains (in
the 0.1% range) when playing with this on derf.
Change-Id: Ib21eed477bbb46bddcd73b21c5c708a5b46abedc
Now that the first AC coefficient in both directions use the same DC
as their context, there no longer is a purpose in letting both have
their own band. Merging these two bands allows us to split bands for
some of the very high-frequency AC bands.
In addition, I'm redoing the banding for the 1D-ADST col/row scans. I
don't think the old banding made any sense at all (it merged the last
coefficient of the first row/col in the same band as the first two of
the second row/col), which was clearly an oversight from the band being
applied in scan-order (rather than in their actual position). Now,
coefficients at the same position will be in the same band, regardless
what scan order is used. I think this makes most sense for the purpose
of banding, which is basically "predict energy for this coefficient
depending on the energy of context coefficients" (i.e. pt).
After full re-training, together with previous patch, derf gains about
1.2-1.3%, and hd/stdhd gain about 0.9-1.0%.
Change-Id: I7a0cc12ba724e88b278034113cb4adaaebf87e0c
Pearson correlation for above or left is significantly higher than for
previous-in-scan-order (absolute values depend on position in scan, but
in general, we gain about 0.1-0.2 by using either above or left; using
both basically just makes this even better). For eob branch skipping,
we continue to use the previous token in scan order.
This helps about 0.9% on derf after re-training on a limited data set.
Full re-training and results on larger-resolution clips are pending.
Note that this commit breaks trellis, so we can probably get further
gains out of it by fixing trellis at some later point.
Change-Id: Iead68e296fc3a105cca746b5e3da9555d6010cfe
Reoptimizes the 8-tap smooth filter.
Results:
derf: +0.101%
yt: +0.157%
hd: +0.791%
stdhd: +0.264%
The next step will be to reoptimize the other two filters.
Change-Id: I3d256a510ad9c7c30c33fae4a70fb43dfc708ed0
Lower case variable names, declaration and initialization on the same line,
removing redundant casts to double.
Change-Id: I7ea3905bed827aa6faac11a78401b85e448b57f9
Lower case variable names, removing redundant variables, declaration and
initialization on the same line.
Change-Id: Ie0c6c95b14103990eb6a9d7784f8259c662e1251
Moving code from vp9_decode_frame function into setup_loopfilter and
setup_segmentation functions. A little bit of cleanup.
Change-Id: I2cce1813e4d7aeec701ccf752bf57e3bdd41b51c
Adds a per-frame, strength adjustable, in loop deringing filter. Uses
the existing vp9_post_proc_down_and_across 5 tap thresholded blur
code, with a brute force search for the threshold.
Results almost strictly positive on the YT HD set, either having no
effect or helping PSNR in the range of 1-3% (overall average 0.8%).
Results more mixed for the CIF set, (-0.5 min, 1.4 max, 0.1 avg).
This has an almost strictly negative impact to SSIM, so examining a
different filter or a more balanced search heuristic is in order.
Other test set results pending.
Change-Id: I5ca6ee8fe292dfa3f2eab7f65332423fa1710b58
Replaces the default tables for single coefficient magnitudes with
those obtained from an appropriate distribution. The EOB node
is left unchanged. The model is represeted as a 256-size codebook
where the index corresponds to the probability of the Zero or the
One node. Two variations are implemented corresponding to whether
the Zero node or the One-node is used as the peg. The main advantage
is that the default prob tables will become considerably smaller and
manageable. Besides there is substantially less risk of over-fitting
for a training set.
Various distributions are tried and the one that gives the best
results is the family of Generalized Gaussian distributions with
shape parameter 0.75. The results are within about 0.2% of fully
trained tables for the Zero peg variant, and within 0.1% of the
One peg variant.
The forward updates are optionally (controlled by a macro)
model-based, i.e. restricted to only convey probabilities from the
codebook. Backward updates can also be optionally (controlled by
another macro) model-based, but is turned off by default. Currently
model-based forward updates work about the same as unconstrained
updates, but there is a drop in performance with backward-updates
being model based.
The model based approach also allows the probabilities for the key
frames to be adjusted from the defaults based on the base_qindex of
the frame. Currently the adjustment function is a placeholder that
adjusts the prob of EOB and Zero node from the nominal one at higher
quality (lower qindex) or lower quality (higher qindex) ends of the
range. The rest of the probabilities are then derived based on the
model from the adjusted prob of zero.
Change-Id: Iae050f3cbcc6d8b3f204e8dc395ae47b3b2192c9
Lower case variable names, code simplification by using already defined
clamp and read_le16 functions.
Change-Id: I8fd544365bd8d1daed86d7b2ae0843e4ef80df08
Changing 0x00 ('') fourcc byte to 0x30 ('0'). For VP8 from
0x00385056 to 0x30385056, for VP9 from 0x00395056 to 0x30395056.
Change-Id: I26b1b603c20dd41f7aeabf8cd7893dfd5b1c8b59
As things stand the zero bin mode boost is hurting somewhat.
In part this seems to be because the boost applied as is
interferes with the rd mode selection loop.
Average gains (derf 0.072, yt 0.243, ythd 0.179 std-hd 0.212%)
Change-Id: Icaecea3908d9a7352370e49b8fa822f2c2c49dc1
Wrote sse2 version of vp9_short_idct10_16x16 function. Compared
to c version, the sse2 version is 2.3X faster.
Change-Id: I314c4f09369648721798321eeed6f58e38857f26
Making consistent initialization of mb_to_{top,botton,left,right}_edge
variables after set_mb_row & set_mb_col calls. A little bit of code cleanup
additionally.
Change-Id: I245bfe32c5701e9836956dc25cf8c770d109cbc1
Wrote sse2 version of vp9_short_idct16x16 function. Compared to c
version, the sse2 version is over 2.5X faster.
Change-Id: I38536e2b846427a2cc5c5423aaf305fd0e605d61
Renaming Width to width, Height to height and Version to version in
several structs and function signatures.
Change-Id: I084c3f7e747cb2ce3345aff27a3dff9b13a87543
Wrote sse2 functions of vp9_short_idct8x8 and vp9_short_idct10_8x8.
Compared to c version, the sse2 version is 2X faster. The decoder
test didn't show noticeable gain since 8x8 idct doesn't take much
of decoding time (less than 1% in my test).
Change-Id: I56313e18cd481700b3b52c4eda5ca204ca6365f3
If the intended display size is different than the size the frame is
coded at, then send that size explicitly in the bitstream. Adds a new
bit to the frame header to indicate whether the extra size fields
are present.
Change-Id: I525c66f22d207efaf1e5f903c6a2a91b80245854
Adjust the filter length and strength for each
ARF group based on a measure of difficulty (the boost)
and the active q range.
Remove lower limit on RDMULT value.
Average gains on the different sets in range 0.4%-0.9%.
However the ARNR changes give a very big boost on a
few clips.
Eg. Soccer ~5%, in derf set and Cyclist ~ 10% in the std-hd set
Change-Id: I2078d78798e27ad2bcc2b32d703ea37b67412ec4
