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image-compression
Commit 279ebc7a authored by Kelly Chang's avatar Kelly Chang
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take out kelly's

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.DS_Store
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Compression_Rate_Kelly.ipynb deleted 100644 → 0
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Encoding_decoding.ipynb
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 34,
"id": "14f74f21",
"metadata": {},
"outputs": [],
......@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 35,
"id": "c16af61f",
"metadata": {},
"outputs": [],
......@@ -78,7 +78,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 36,
"id": "aceba613",
"metadata": {},
"outputs": [],
......@@ -139,7 +139,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 37,
"id": "6b965751",
"metadata": {},
"outputs": [],
......@@ -192,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 38,
"id": "b7561883",
"metadata": {},
"outputs": [],
......@@ -300,14 +300,23 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 39,
"id": "2eb774d2",
"metadata": {},
"outputs": [],
"source": [
"def encoder(error, list_dic, diff, bound, bins):\n",
" \"\"\"\n",
" This function en\n",
" This function encode the matrix with huffman coding tables\n",
" \n",
" Input:\n",
" error (512, 640): a matrix with all the errors\n",
" list_dic (num_dic + 1,): a list of huffman coding table \n",
" bound (2300,): the boundary values after subtracting the very first pixel value\n",
" bins (num_bins - 1,): a list of threshold to cut the bins\n",
" \n",
" Return:\n",
" encoded (512, 640): encoded matrix\n",
" \"\"\"\n",
" # copy the error matrix (including the boundary)\n",
" encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
......@@ -331,15 +340,21 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 40,
"id": "8eeb40d0",
"metadata": {},
"outputs": [],
"source": [
"def decoder(A, encoded_matrix, list_dic, bins):\n",
" \"\"\"\n",
" Function that accecpts the prediction matrix A for the linear system,\n",
" the encoded matrix of error values, and the encoding dicitonary.\n",
" This function decodes the encoded_matrix.\n",
" Input:\n",
" A (3 X 3): system of equation\n",
" list_dic (num_dic + 1,): a list of huffman coding table \n",
" encoded_matrix (512, 640): encoded matrix\n",
" bins (num_bins - 1,): a list of threshold to cut the bins\n",
" Return:\n",
" decode_matrix (512, 640): decoded matrix\n",
" \"\"\"\n",
" # change the dictionary back to list\n",
" # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
......@@ -358,23 +373,23 @@
" the_keys4 = list(list_dic[4].keys())\n",
" the_values4 = list(list_dic[4].values())\n",
" \n",
" error_matrix = np.zeros((512,640))\n",
" decode_matrix = np.zeros((512,640))\n",
" # loop through all the element in the matrix\n",
" for i in range(error_matrix.shape[0]):\n",
" for j in range(error_matrix.shape[1]):\n",
" for i in range(decode_matrix.shape[0]):\n",
" for j in range(decode_matrix.shape[1]):\n",
" # if it's the very first pixel on the image\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" decode_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" # if it's on the boundary\n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" elif i == 0 or i == decode_matrix.shape[0]-1 or j == 0 or j == decode_matrix.shape[1]-1:\n",
" decode_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + decode_matrix[0][0]\n",
" # if not the boundary\n",
" else:\n",
" # predict the image with the known pixel value\n",
" z0 = error_matrix[i-1][j-1]\n",
" z1 = error_matrix[i-1][j]\n",
" z2 = error_matrix[i-1][j+1]\n",
" z3 = error_matrix[i][j-1]\n",
" z0 = decode_matrix[i-1][j-1]\n",
" z1 = decode_matrix[i-1][j]\n",
" z2 = decode_matrix[i-1][j+1]\n",
" z3 = decode_matrix[i][j-1]\n",
" y0 = int(-z0+z2-z3)\n",
" y1 = int(z0+z1+z2)\n",
" y2 = int(-z0-z1-z2-z3)\n",
......@@ -385,26 +400,30 @@
" # add on the difference by searching the dictionary\n",
" # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
" if difference <= bins[0]:\n",
" error_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" decode_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
" error_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
" decode_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[2] and difference > bins[1]:\n",
" error_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
" decode_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
" else:\n",
" error_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
" decode_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
" \n",
" \n",
" return error_matrix.astype(int)"
" return decode_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 41,
"id": "f959fe93",
"metadata": {},
"outputs": [],
"source": [
"def compress_rate(image, error, diff, bound, list_dic, bins):\n",
" '''\n",
" This function is used to calculate the compression rate.\n",
" \n",
" '''\n",
" # the bits for the original image\n",
" o_len = 0\n",
" # the bits for the compressed image\n",
......@@ -444,7 +463,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 42,
"id": "3e0e9742",
"metadata": {},
"outputs": [
......@@ -470,7 +489,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 33,
"id": "004e8ba8",
"metadata": {},
"outputs": [
......@@ -478,13 +497,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[26, 40, 62]\n"
"(512, 640)\n"
]
}
],
"source": [
"\n",
"print(bins)"
"print(encoded_matrix.shape)"
]
},
{
......
Error_to_Image.ipynb deleted 100644 → 0
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im/.DS_Store 0 → 100644
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multiple_compression_kelly.ipynb deleted 100644 → 0
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prediction_MSE_kelly.ipynb deleted 100644 → 0
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