)]}'
{
  "commit": "ff2ffee30b43ba9308801d8938dd6c3bf611d068",
  "tree": "807ac79f7e52e49ed12e2785cf92edde7a801904",
  "parents": [
    "4ad372c580625feec390271156904ee1603ab458"
  ],
  "author": {
    "name": "Yunqing Wang",
    "email": "yunqingwang@google.com",
    "time": "Fri Oct 16 01:25:46 2020 -0700"
  },
  "committer": {
    "name": "Leo (Liang) Zhao",
    "email": "leolzhao@tencent.com",
    "time": "Thu Dec 10 23:45:00 2020 +0000"
  },
  "message": "Improve temporal filter prediction\n\nIn temporal filtering, applied high precision 12-tap filter in\nprediction process. This gave a good coding gain.\n\nBorg test result at speed 1:\n        avg_psnr  ovr_psnr  ssim\nlowres2: -0.526    -0.517  -0.337\nmidres2: -0.359    -0.349  -0.161\nhdres2:  -0.168    -0.162  -0.083\n\nTODO: Need to add 12-tap SIMD code, so the encoder slowness can\nbe mostly reduced.\n\nSTATS_CHANGED\n\nChange-Id: I88c7dcd2c9afe00d52e5299b1330f42c6e1e01cb\n(cherry picked from commit 144a941ec51cb998ea7923445e183bbaf048a777)\n",
  "tree_diff": [
    {
      "type": "modify",
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      "old_mode": 33188,
      "old_path": "av1/common/convolve.c",
      "new_id": "716886fc98b030a444e6dc89799c5ac3c6698159",
      "new_mode": 33188,
      "new_path": "av1/common/convolve.c"
    },
    {
      "type": "modify",
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      "old_mode": 33188,
      "old_path": "av1/common/filter.h",
      "new_id": "16a94506eb01e608caf95006a24651d23dfa0dde",
      "new_mode": 33188,
      "new_path": "av1/common/filter.h"
    },
    {
      "type": "modify",
      "old_id": "77d4a231c3640c5096da7c3bf7bb802fa3185b25",
      "old_mode": 33188,
      "old_path": "av1/encoder/temporal_filter.c",
      "new_id": "922b9ae3b746aeb5b1cb96c0e52988856b3348bf",
      "new_mode": 33188,
      "new_path": "av1/encoder/temporal_filter.c"
    },
    {
      "type": "modify",
      "old_id": "e87f914b149f634cea18c5ad35997a5fe7b3b4a6",
      "old_mode": 33188,
      "old_path": "test/av1_convolve_test.cc",
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      "new_mode": 33188,
      "new_path": "test/av1_convolve_test.cc"
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  ]
}