)]}'
{
  "commit": "a01b177859a1cc85a7b4d90590b0e1b2fd147d6c",
  "tree": "a080e1669248afdcf4d2e0514716b70d063b5838",
  "parents": [
    "e82c671721bfadf626613ed6341261419ac3d4ac"
  ],
  "author": {
    "name": "yjshen",
    "email": "yjshen@google.com",
    "time": "Thu Apr 09 12:42:42 2020 -0700"
  },
  "committer": {
    "name": "Damon Shen",
    "email": "yjshen@google.com",
    "time": "Tue Apr 14 04:18:04 2020 +0000"
  },
  "message": "Consider motion search error in plane-wise filter.\n\nIn plane-wise temporal filtering strategy, filter weight is assigned\nbased on motion search. Currently, only a small neighborhood (i.e., a\n5x5 window) is considered for each individual pixel. However, the motion\nsearch result of the entire block also reflects the search accuracy.\n\nThis CL improves the plane-wise strategy by considering both local (5x5\nwindow) and global (the entire filtering block) information to assign\nfilter weight. In particular, the window-wise error plays a more\nimportant role than the block-wise error, but both of them are involved\nin the filtering process.\n\nNOTE: This CL only affects the performance on midres and hdres datasets.\n\nExperimental results:\n\nUnder Speed-4 (two-pass mode):\n          avg PSNR   ovr PSNR     SSIM\nmidres      -0.056     -0.015   -0.026\nmidres2     -0.029      0.000   -0.000\nhdres       -0.085     -0.076   -0.049\nhdres2      -0.050     -0.028   -0.017\n\nUnder Speed-1 (two-pass mode):\n\t  avg PSNR   ovr PSNR     SSIM\nmidres      -0.129     -0.033   -0.036\nmidres2     -0.074     -0.044   -0.026\nhdres       -0.106     -0.091   -0.098\nhdres2      -0.070     -0.055   -0.057\n\nSTATS_CHANGED\n\nChange-Id: Ie883a0105b1bdf4cd41b40b2e5be7f2f7178dd50\n",
  "tree_diff": [
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