rav1e_v5.1_vs_v6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6233fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12.76409276,  0.8369    ],\n",
       "       [ 4.68797593,  0.8448    ],\n",
       "       [ 2.66943043,  0.8696    ],\n",
       "       [ 1.19103978,  0.899     ],\n",
       "       [ 0.8846055 ,  0.9187    ],\n",
       "       [ 0.79378616,  0.9284    ],\n",
       "       [ 0.67381358,  0.9668    ],\n",
       "       [ 0.6324838 ,  0.9721    ],\n",
       "       [ 0.59647621,  0.9801    ],\n",
       "       [ 0.50039305,  1.1469    ],\n",
       "       [ 0.25321587,  1.507     ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import seaborn as sns\n",
    "import struct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"darkgrid\", context=\"talk\")\n",
    "plt.style.use(\"dark_background\")\n",
    "plt.rcParams.update({\"grid.linewidth\":0.5, \"grid.alpha\":0.5})\n",
    "\n",
    "v5_1 = np.array([[18.2344, 0.8817], [7.6955, 0.8927], [3.4063, 0.9219],\n",
    "                 [2.1284, 0.9350], [1.6182, 0.9551], [1.4241, 0.9646],\n",
    "                 [1.0000, 1.0000], [1.0036, 1.0013], [1.0031, 1.0056],\n",
    "                 [0.7193, 1.3452], [0.3498, 1.5710]])\n",
    "\n",
    "v6 = np.array([\n",
    "    [1 + 17.3853, 1 - .1631],\n",
    "    [1 + 5.3302, 1 - .1552],\n",
    "    [1 + 1.926, 1 - .1304],\n",
    "    [1 + .5738, 1 - .1010],\n",
    "    [1 + .1671, 1 - .0813],\n",
    "    [1 + .1038, 1 - .0716],\n",
    "    [1 - .2360, 1 - .0332],\n",
    "    [1 - .2661, 1 - .0279],\n",
    "    [1 - .3394, 1 - .0199],\n",
    "    [1 - .4544, 1 + .1469],\n",
    "    [1 - .7197, 1 + .5070],\n",
    "])\n",
    "\n",
    "relspeed = np.array([1.42857, 1.64154, 1.27604, 1.78701, 1.82929, 1.79406, 1.48409, 1.58676, 1.68171, 1.43747, 1.38143])\n",
    "\n",
    "v6.T[0] = v5_1.T[0] / relspeed\n",
    "v6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0915f6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 812,
       "width": 1371
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.plot(*v5_1.T, 's-', label='v0.5.1')\n",
    "ax.plot(*v6.T, 'D-', label='v0.6.6 (avx2)')\n",
    "for i in range(11):\n",
    "    ax.annotate(i, v5_1[i], v5_1[i] + .01)\n",
    "    ax.annotate(i, v6[i], v6[i] + .01)\n",
    "\n",
    "ax.set_xlabel('⟵ Faster\\nRelative time')\n",
    "ax.set_ylabel('Relative bitrate\\n⟵ Smaller')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2cdf3a5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 810,
       "width": 1371
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.plot(*(1 / v5_1).T, 's-', label='v0.5.1')\n",
    "ax.plot(*(1 / v6).T, 'D-', label='v0.6.6 (avx2)')\n",
    "for i in range(11):\n",
    "    ax.annotate(i, (1 / v5_1)[i], (1 / v5_1)[i] + .01)\n",
    "    ax.annotate(i, (1 / v6)[i], (1 / v6)[i] + .01)\n",
    "\n",
    "ax.set_xlabel('Faster ⟶\\nRelative speed')\n",
    "ax.set_ylabel('Relative compression\\nSmaller ⟶')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5aabe960",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scales(frame, name):\n",
    "    with open(\"objective-1-fast/.lookahead_data//%010d-%s_scales.bin\" % (frame, name), \"rb\") as f:\n",
    "        contents_batch = f.read()\n",
    "        cols, rows = struct.unpack(\n",
    "            \"qq\", contents_batch[:16]\n",
    "        )\n",
    "        return np.frombuffer(\n",
    "            contents_batch[16:], dtype=np.uint32\n",
    "        ).reshape((rows, cols))\n",
    "        \n",
    "def load_thresholds(frame):\n",
    "    with open(\"objective-1-fast/.lookahead_data//%010d-thresholds.bin\" % frame, \"rb\") as f:\n",
    "        contents_batch = f.read()\n",
    "        count_minus_1 = struct.unpack(\n",
    "            \"q\", contents_batch[:8]\n",
    "        )\n",
    "        return np.frombuffer(\n",
    "            contents_batch[8:], dtype=np.uint32\n",
    "        )\n",
    "\n",
    "def map_segments(spatiotemporal_scale, threshold):\n",
    "    segments = spatiotemporal_scale.copy()\n",
    "    all_threhold = [segments.max() + 1] + threshold.tolist() + [segments.min() - 1]\n",
    "    for (hi, lo) in zip(all_threhold, all_threhold[1:]):\n",
    "        idx = (segments < hi) * (segments >= lo)\n",
    "        mean = segments[idx].mean()\n",
    "        segments[idx] = mean\n",
    "    return segments\n",
    "\n",
    "spatial_scale = load_scales(0, 'activity')\n",
    "temporal_scale = load_scales(0, 'distortion')\n",
    "spatiotemporal_scale = load_scales(0, 'spatiotemporal')\n",
    "threshold = load_thresholds(0)\n",
    "segment = map_segments(spatiotemporal_scale, threshold)\n",
    "segment_30 = map_segments(load_scales(30, 'spatiotemporal'), load_thresholds(30))\n",
    "\n",
    "vmin = min(spatial_scale.min(), temporal_scale.min(), spatiotemporal_scale.min())\n",
    "vmax = max(spatial_scale.max(), temporal_scale.max(), spatiotemporal_scale.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99800a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('Spatial Scale')\n",
    "plt.imshow(spatial_scale, vmin=vmin, vmax=vmax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1988ba28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('Temporal Scale')\n",
    "plt.imshow(temporal_scale, vmin=vmin, vmax=vmax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79b6aa6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('Spatiotemporal Scale')\n",
    "plt.imshow(spatiotemporal_scale, vmin=vmin, vmax=vmax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd389425",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('Segment')\n",
    "plt.imshow(segment, vmin=vmin, vmax=vmax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "76133f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 523,
       "width": 884
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_title('Segment (Frame 30)')\n",
    "plt.imshow(segment_30, vmin=vmin, vmax=vmax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b52e2c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ssim_boost(x, y):\n",
    "    return 3355. / 12338. * (8128.125 * (x + y) + 16128.) / np.sqrt(4064.0625**2 * x * y + 11256025.)\n",
    "\n",
    "x = np.linspace(0, 6, 301)\n",
    "y = np.linspace(0, 6, 301)\n",
    "X, Y = np.meshgrid(x, y)\n",
    "Z = ssim_boost(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af48c4f8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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