)]}'
{
  "commit": "76930e9156df0acce1aeb171dd28758a095f1d33",
  "tree": "daac388316aa729d1599439bce6b0a2cad2c8ecb",
  "parents": [
    "0292e2e9a5679d815c5d855d6f166b5d4b8a6d20"
  ],
  "author": {
    "name": "yjshen",
    "email": "yjshen@google.com",
    "time": "Thu Apr 09 16:52:33 2020 -0700"
  },
  "committer": {
    "name": "Damon Shen",
    "email": "yjshen@google.com",
    "time": "Tue Apr 14 19:40:58 2020 +0000"
  },
  "message": "Consider quantization factor in plane-wise filter.\n\nIn plane-wise temporal filtering strategy, filter weight is assigned\nindependently from the quantization factor used in video compression.\nHowever, when we use small q (i.e., with high bitrate), a weaker\nfiltering strength is expected since we would like to keep more details.\n\nThis CL improves the plane-wise strategy by considering the quantization\nfactor for filter weight assignment. In particular, when q is large\nenough (\u003e\u003d16), nothing is changed. When q is less than 16, we will\nreduce the filtering strength w.r.t. the q-value. This change\nsignificantly improves the performance on high-bitrate encoding.\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      -0.116     -0.099   -0.040\nmidres2     -0.047     -0.044   -0.017\nhdres       -0.142     -0.197   -0.075\nhdres2      -0.010     -0.012   -0.004\n\nUnder Speed-1 (two-pass mode):\n          avg PSNR   ovr PSNR     SSIM\nmidres      -0.132     -0.124   -0.040\nmidres2     -0.057     -0.053   -0.020\nhdres       -0.158     -0.177   -0.066\nhdres2      -0.014     -0.018   -0.005\n\nSTATS_CHANGED\n\nChange-Id: I486e66770a4454fb1f72c3276cd20a4b3ae3dd3d\n",
  "tree_diff": [
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