1. 26 Mar, 2013 4 commits
    • 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
  2. 22 Mar, 2013 6 commits
  3. 21 Mar, 2013 3 commits
  4. 20 Mar, 2013 1 commit
  5. 19 Mar, 2013 2 commits
  6. 18 Mar, 2013 15 commits
  7. 17 Mar, 2013 1 commit
  8. 16 Mar, 2013 4 commits
  9. 15 Mar, 2013 4 commits