1. 01 Apr, 2013 1 commit
    • Ronald S. Bultje's avatar
      Calculate SSIM over both reconstruction as well as postproc buffer. · 6dd6ffb0
      Ronald S. Bultje authored
      We used to calculate SSIM only over the postproc buffer, whereas we
      calculate PSNR for both. Compared to postproc-SSIM, this is about 0.3%
      higher for derf, 1.4% lower for hd and 0.5% lower for stdhd, although
      it is highly variable on a per-clip basis.
      
      Change-Id: I8dd491f0f5b4201dedfb15d288c854d5d4caa10f
      6dd6ffb0
  2. 28 Mar, 2013 1 commit
    • Ronald S. Bultje's avatar
      Fix mix-up in pt token indexing. · 9eea9fa2
      Ronald S. Bultje authored
      This fixes uninitialized reads in the trellis, and probably makes the
      trellis do something again.
      
      Change-Id: Ifac8dae9aa77574bde0954a71d4571c5c556df3c
      9eea9fa2
  3. 27 Mar, 2013 6 commits
  4. 26 Mar, 2013 15 commits
    • Deb Mukherjee's avatar
      Implicit weighted prediction experiment · 23144d23
      Deb Mukherjee authored
      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
      23144d23
    • Ronald S. Bultje's avatar
      Add col/row-based coefficient scanning patterns for 1D 8x8/16x16 ADSTs. · d9094d8f
      Ronald S. Bultje authored
      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
      d9094d8f
    • Ronald S. Bultje's avatar
      Redo banding for all transforms. · 3120dbdd
      Ronald S. Bultje authored
      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
      3120dbdd
    • Ronald S. Bultje's avatar
      Use above/left (instead of previous in scan-order) as token context. · 790fb132
      Ronald S. Bultje authored
      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
      790fb132
    • John Koleszar's avatar
      64661660
    • Dmitry Kovalev's avatar
      Cleaning up loopfilter code. · d7209b3a
      Dmitry Kovalev authored
      Lower case variable names, removing redundant variables, declaration and
      initialization on the same line.
      
      Change-Id: Ie0c6c95b14103990eb6a9d7784f8259c662e1251
      d7209b3a
    • Dmitry Kovalev's avatar
      Decomposition of vp9_decode_frame function. · 4a3d7860
      Dmitry Kovalev authored
      Moving code from vp9_decode_frame function into setup_loopfilter and
      setup_segmentation functions. A little bit of cleanup.
      
      Change-Id: I2cce1813e4d7aeec701ccf752bf57e3bdd41b51c
      4a3d7860
    • John Koleszar's avatar
    • John Koleszar's avatar
      Merge "Code cleanup." into experimental · 7d9a7fb2
      John Koleszar authored
      7d9a7fb2
    • John Koleszar's avatar
      Merge "Code cleanup." into experimental · f0923f3b
      John Koleszar authored
      f0923f3b
    • John Koleszar's avatar
    • John Koleszar's avatar
      Add an in-loop deringing experiment · 441e2eab
      John Koleszar authored
      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
      441e2eab
    • Deb Mukherjee's avatar
      Bugfix in model coef prob experiment · d14c7265
      Deb Mukherjee authored
      Fixes an issue with model based update that got into
      the original patch that was merged.
      
      Change-Id: Ie42d3d0aff2e48cd187d96664dbd3e9d6d3ac22f
      d14c7265
    • Deb Mukherjee's avatar
    • Deb Mukherjee's avatar
      Modeling default coef probs with distribution · fd18d5df
      Deb Mukherjee authored
      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
      fd18d5df
  5. 25 Mar, 2013 2 commits
    • Dmitry Kovalev's avatar
      Code cleanup. · 3644a5b6
      Dmitry Kovalev authored
      Fixing function arguments alignment, reusing MIN/MAX and clamp functions.
      
      Change-Id: I87dd5a40ffb65b521b8abbf0fccf2f50552c5309
      3644a5b6
    • Dmitry Kovalev's avatar
      Code cleanup. · 7cc14e59
      Dmitry Kovalev authored
      Lower case variable names, code simplification by using already defined
      clamp and read_le16 functions.
      
      Change-Id: I8fd544365bd8d1daed86d7b2ae0843e4ef80df08
      7cc14e59
  6. 22 Mar, 2013 6 commits
  7. 21 Mar, 2013 4 commits
  8. 20 Mar, 2013 1 commit
  9. 19 Mar, 2013 2 commits
  10. 18 Mar, 2013 2 commits