Speed-wise equivalent on x86 and ARM (maybe a tad faster, hard to tell).
Note that the two 32-bit multiples are not strictly equivalent
to the 64-bit one, since we're missing one carry propagation.
In practice, no observable difference was seen because of this
slightly different hashing result.
Change-Id: I8f2381175eae1cb20dabf149e6b27e1768fba6ab
had to rename few structs.
-> we can now include both vp8i.h and vp8enci.h without naming
conflicts.
Change-Id: Ib41b498f1b57aab3d6b796361afc45210ec75174
we look at average global improvement and stop when things are
moving slow, or when we had a quite good first iteration already
(means: the picture is "not difficult")
Change-Id: I8ab7d100353039b5b32bb5fac3fe03c8440c78d5
Speedup method StoreImageToBitMask by replacing the code to find histogram
index and Huffman tree codes at every iteration to a more optimal code that
updates these only when the current pixel (to write) crosses the histogram
tile-row boundary.
This change speeds up the StoreImageToBitMask method by 5%.
Change-Id: If01a1ccd7820f9a3a3e5bc449d070defa51be14b
The MIPS code for cost is not updated yet, that's why i keep Residual::*cost
around for now. Should be removed in favor of *costs later.
Change-Id: Id1d09a8c37ea8c5b34ad5eb8811d6a3ec6c4d89f
removes circular dependency between dsp and enc.
since:
a987fae MIPS: dspr2: added optimization for function GetResidualCost
Change-Id: Ifeb8fc02de89e2ba982ed7ffacd925d649bfec3c
kGammaFix is now only defined with USE_GAMMA_COMPRESSION;
fixes:
use of undeclared identifier 'kGammaFix'
Change-Id: Ib1e2f410eff9b83be065894f88181f91dd2776e1
set/get residual C functions moved to new file in src/dsp
mips32 version of GetResidualCost moved to new file
Change-Id: I7cebb7933a89820ff28c187249a9181f281081d2
the input to the function is non-const and the pointer being operated is
being free'd; removes an unnecessary cast in the process
Change-Id: Ic515ed672ddf7f8e4e36eeac696ff7aa8a3652f7
Updated the near-lossless level mapping and make it correlated to lossy
quality i.e 100 => minimum loss (in-fact no-loss) and the visual-quality loss
increases with decrease in near-lossless level (quality) till value 0.
The new mapping implies following (PSNR) loss-metric:
-near_lossless 100: No-loss (bit-stream same as -lossless).
-near_lossless 80: Very very high PSNR (around 54dB).
-near_lossless 60: Very high PSNR (around 48dB).
-near_lossless 40: High PSNR (around 42dB).
-near_lossless 20: Moderate PSNR (around 36dB).
-near_lossless 0: Low PSNR (around 30dB).
Change-Id: I930de4b18950faf2868c97d42e9e49ba0b642960
AnalyzeSubtractGreen constitutes about 8-10% of the comression CPU cycles.
Statistically, subtract-green is proved to be useful for most of the
non-palette compression. So instead of evaluating the entropy (by calling
AnalyzeSubtractGreen) apply subtract-green transform for the low-effort
compression.
This changes speeds up the compression at m=0 by 8-10% (with very slight loss
of 0.07% in the compression density).
Change-Id: I9797dc39437ae089716acb14631bbc77d367acf4
Speed up AnalyzeSubtractGreen by looping through the image pixel once to
compute the two histograms.
AnalyzeEntropy code cleanup.
Removed some 'if' conditions and pointer indirections inside pixel iterate loop.
Change-Id: Ia65e3033988ff67df8e3ecce19d6e34cfc76358e
Enable the WebP near-lossless feature by pre-processing the image to smoothen
the pixels.
On a 1000 PNG image corpus, for which WebP lossless (default settings) gets
25% compression gains, following is the performance of near-lossless feature
at various '-near_lossless' levels:
-near_lossless 90: 30% (very very high PSNR 54-60dB)
-near_lossless 75: 38% (very high PSNR 48-54dB)
-near_lossless 50: 45% (high PSNR 42-48dB)
-near_lossless 25: 48% (moderate PSNR 36-42dB)
-near_lossless 10: 50% (PSNR 30-36dB)
WebP near-lossless is specifically useful for discrete-tone images like
line-art, icons etc.
Change-Id: I7d12a2c9362ccd076d09710ea05c85fa64664c38
Simplify and speedup backward references for low-effort settings by evaluating
LZ77 references only. This change speeds up compression by 10-25% at lower
(q <= 25) quality range with a slight drop (0.2%) in the compression density.
Change-Id: Ibd6f03b1a062d8ab9191786c2a425e9132e4779f
Cleaup Near-lossless code
- Simplified and refactored the code.
- Removed the requirement (TODO) to allocate the buffer of size WxH and work
with buffer of size 3xW.
- Disabled the Near-lossless prr-processing for small icon images (W < 64 and H < 64).
Change-Id: Id7ee90c90622368d5528de4dd14fd5ead593bb1b
* use the same TFIX == YFIX precision (2bits)
* use int instead of float in LinearToGammaF()
output is visually equivalent. Code is a little faster.
Change-Id: Ie3cfebca351dbcbd924b3d00801d6523dca6981f
check enc->argb_ to quiet an msvs /analyze warning:
C6387: 'enc->argb_+y*width' could be '0': this does not adhere to the
specification for the function 'memcpy'.
Change-Id: I87544e92ee0d3ea38942a475c30c6d552f9877b7
Disable costly TraceBackwards heuristic for computing the backward references
for low_effort (method=0) compression.
The TraceBackwards heuristic is already disabled for lower (q < 25) quality
range. Following is the compression data for 1000 image corpus for q >= 25.
This speeds up compression (q >= 25) by a factor of 2.5-3X with slight loss of
compression density (0.7% for lower quality range and 1.2% for higher qualities).
Change-Id: I256c9e2137c7de4083f423ea32ee12d3b0f46253
- Lower the threshold parameters for HashChainFindCopy.
For 1000 image PNG corpus (m=0), this change yields speedup of 15-20% at
lower quality range (0.25% drop in compression density) and about 10%
for higher quality range without any drop in the compression density.
Following is the compression stats (before/after) for method = 0:
Before After
bpp/MPs bpp/MPs
q=0 2.8615/18.000 2.8651/18.631
q=5 2.8615/18.216 2.8650/20.517
q=10 2.8572/18.070 2.8650/21.992
q=15 2.8519/18.371 2.8584/21.747
q=20 2.8454/18.975 2.8515/20.448
q=25 2.8230/8.531 2.8253/9.585
// Compression density remains same for q-range [30-100]
q=30 2.7310/7.706 2.7310/8.028
q=35 2.7253/6.855 2.7253/7.184
q=40 2.7231/6.364 2.7231/6.604
q=45 2.7216/5.844 2.7216/6.223
q=50 2.7196/5.210 2.7196/5.731
q=55 2.7208/4.766 2.7208/4.970
q=60 2.7195/4.495 2.7195/4.602
q=65 2.7185/4.024 2.7185/4.236
q=70 2.7174/3.699 2.7174/3.861
q=75 2.7164/3.449 2.7164/3.605
q=80 2.7161/3.222 2.7161/3.038
q=85 2.7153/2.919 2.7153/2.946
q=90 2.7145/2.766 2.7145/2.771
q=95 2.7124/2.548 2.7124/2.575
q=100 2.6873/2.253 2.6873/2.335
Change-Id: I0e17581fb71f6094032ad06c6203350bd502f9a1
- Do light weight entropy based histogram combine and leave out CPU
intensive stochastic and greedy heuristics for combining the
histograms.
For 1000 image PNG corpus (m=0), this change yields speedup of 10% at
lower quality range (1% drop in compression density) and about 5% for
higher quality range (1% drop in compression density). Following is the
compression stats (before/after) for method = 0:
Before After
bpp/MPs bpp/MPs
q=0 2.8336/16.577 2.8615/18.000
q=5 2.8336/16.504 2.8615/18.216
q=10 2.8293/16.419 2.8572/18.070
q=15 2.8242/17.582 2.8519/18.371
q=20 2.8182/16.131 2.8454/18.975
q=25 2.7924/7.670 2.8230/8.531
q=30 2.7078/6.635 2.7310/7.706
q=35 2.7028/6.203 2.7253/6.855
q=40 2.7005/6.198 2.7231/6.364
q=45 2.6989/5.570 2.7216/5.844
q=50 2.6970/5.087 2.7196/5.210
q=55 2.6963/4.589 2.7208/4.766
q=60 2.6949/4.292 2.7195/4.495
q=65 2.6940/3.970 2.7185/4.024
q=70 2.6929/3.698 2.7174/3.699
q=75 2.6919/3.427 2.7164/3.449
q=80 2.6918/3.106 2.7161/3.222
q=85 2.6909/2.856 2.7153/2.919
q=90 2.6902/2.695 2.7145/2.766
q=95 2.6881/2.499 2.7124/2.548
q=100 2.6873/2.253 2.6873/2.285
Change-Id: I0567945068f8dc7888041e93d872f9def91f50ba
inline function MakeARGB32 calls changed to call
via pointers to functions which make (a)rgb for
entire row
Change-Id: Ia4bd4be171a46c1e1821e408b073ff5791c587a9
most of the time, we don't need to actually move the
data.
Compression is randomly slightly different, because HistogramCompactBins() changed.
Timing is about the same.
Change-Id: Ia6af8e9780581014d6860f2b546189ac817cfad1
we don't need to store the whole distribution in order to compute the alpha
Later, we can incorporate the max_value / last_non_zero bookkeeping
in SSE2 directly.
Change-Id: I748ccea4ac17965d7afcab91845ef01be3aa3e15
