diff --git a/.gitignore b/.gitignore
index 02d78242..d067f8e7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,7 @@ examples/data/cifar*
 data/plenoptic-test-files
 data/ssim_images
 data/ssim_analysis.mat
+data/msssim_images
 data/cat7*
 data/elep*
 docs/_build
diff --git a/examples/09_Original_MAD.ipynb b/examples/09_Original_MAD.ipynb
index 065d4594..f742d686 100644
--- a/examples/09_Original_MAD.ipynb
+++ b/examples/09_Original_MAD.ipynb
@@ -70,16 +70,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/billbrod/Documents/plenoptic/plenoptic/metric/perceptual_distance.py:92: UserWarning: dynamic_range is 1 but image range falls outside [0, 1] img1: tensor([0.0039, 1.0000]), img2: tensor([-10.7873,  12.8881]). Continuing anyway...\n",
+      "/home/billbrod/Documents/plenoptic/plenoptic/metric/perceptual_distance.py:40: UserWarning: dynamic_range is 1 but image range falls outside [0, 1] img1: tensor([0.0039, 1.0000]), img2: tensor([-11.3368,  12.8298]). Continuing anyway...\n",
       "  warnings.warn(\"dynamic_range is 1 but image range falls outside [0, 1]\"\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "tensor([[0.0030],\n",
-       "        [0.0014],\n",
-       "        [0.0005]])"
+       "tensor([[0.0026],\n",
+       "        [0.0013],\n",
+       "        [0.0006]])"
       ]
      },
      "execution_count": 3,
@@ -135,11 +135,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "On device cpu\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'/home/billbrod/Documents/plenoptic/tests/../data/ssim_images'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -165,7 +168,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<PyrFigure size 1005x524.667 with 10 Axes>"
       ]
@@ -193,8 +196,8 @@
     {
      "data": {
       "text/plain": [
-       "{'L128': {'FIX_MSE': 128,\n",
-       "  'FIX_SSIM': 0.7735827815185419,\n",
+       "{'L128': {'FIX_MSE': 127.99999999999999,\n",
+       "  'FIX_SSIM': 0.8183184633106257,\n",
        "  'mse_fixmse_maxssim': array([128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
@@ -205,26 +208,26 @@
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128.]),\n",
-       "  'ssim_fixmse_maxssim': array([0.78662261, 0.80209889, 0.82022919, 0.84059707, 0.86155803,\n",
-       "         0.88088954, 0.89727039, 0.91051589, 0.92100997, 0.92940474,\n",
-       "         0.9362193 , 0.94185083, 0.94659523, 0.95063439, 0.95413286,\n",
-       "         0.95719831, 0.95990341, 0.9623143 , 0.96447601, 0.9664242 ,\n",
-       "         0.96819031, 0.96980086, 0.97127809, 0.97264031, 0.97390206,\n",
-       "         0.97507535, 0.97616991, 0.97719364, 0.97815312, 0.97905376,\n",
-       "         0.97990006, 0.98069591, 0.98144479, 0.98214989, 0.98281421,\n",
-       "         0.98344056, 0.98403163, 0.98458993, 0.98511783, 0.98561751,\n",
-       "         0.98609098, 0.98654006, 0.98696643, 0.98737158, 0.98775684,\n",
-       "         0.98812343, 0.98847241, 0.98880476, 0.98912134, 0.98942293,\n",
-       "         0.98971023, 0.98998388, 0.99024446, 0.99049253, 0.99072857,\n",
-       "         0.99095307, 0.99116648, 0.99136922, 0.99156172, 0.99174435,\n",
-       "         0.99191752, 0.99208158, 0.99223692, 0.99238388, 0.99252281,\n",
-       "         0.99265406, 0.99277796, 0.99289483, 0.99300498, 0.99310872,\n",
-       "         0.99320635, 0.99329817, 0.99338446, 0.99346551, 0.99354157,\n",
-       "         0.99361291, 0.99367977, 0.99374241, 0.99380104, 0.99385588,\n",
-       "         0.99390716, 0.99395508, 0.99399982, 0.99404157, 0.99408051,\n",
-       "         0.99411681, 0.99415062, 0.9941821 , 0.99421139, 0.99423862,\n",
-       "         0.99426393, 0.99428744, 0.99430925, 0.99432948, 0.99434822,\n",
-       "         0.99436559, 0.99438166, 0.99439652, 0.99441024, 0.99442292]),\n",
+       "  'ssim_fixmse_maxssim': array([0.82669306, 0.83641599, 0.84768936, 0.86021352, 0.87332037,\n",
+       "         0.8861153 , 0.89794336, 0.90864194, 0.91828312, 0.9270046 ,\n",
+       "         0.93495769, 0.94226293, 0.94896492, 0.95506452, 0.9605342 ,\n",
+       "         0.96533558, 0.96945409, 0.97290935, 0.97575609, 0.97807185,\n",
+       "         0.97994388, 0.98145704, 0.98268627, 0.98369369, 0.98452855,\n",
+       "         0.98522884, 0.98582351, 0.98633451, 0.98677852, 0.98716831,\n",
+       "         0.98751374, 0.98782249, 0.98810062, 0.98835294, 0.98858334,\n",
+       "         0.98879493, 0.98899028, 0.98917148, 0.98934029, 0.98949816,\n",
+       "         0.9896463 , 0.98978576, 0.98991742, 0.99004203, 0.99016023,\n",
+       "         0.99027259, 0.99037961, 0.9904817 , 0.99057924, 0.99067257,\n",
+       "         0.99076198, 0.99084774, 0.99093007, 0.9910092 , 0.99108531,\n",
+       "         0.99115858, 0.99122917, 0.99129722, 0.99136287, 0.99142623,\n",
+       "         0.99148744, 0.99154658, 0.99160377, 0.99165909, 0.99171263,\n",
+       "         0.99176448, 0.9918147 , 0.99186338, 0.99191058, 0.99195637,\n",
+       "         0.99200081, 0.99204396, 0.99208587, 0.99212661, 0.99216622,\n",
+       "         0.99220476, 0.99224226, 0.99227877, 0.99231435, 0.99234902,\n",
+       "         0.99238284, 0.99241583, 0.99244804, 0.99247949, 0.99251023,\n",
+       "         0.99254029, 0.99256969, 0.99259847, 0.99262665, 0.99265426,\n",
+       "         0.99268134, 0.99270789, 0.99273395, 0.99275955, 0.99278469,\n",
+       "         0.99280941, 0.99283372, 0.99285765, 0.9928812 , 0.99290441]),\n",
        "  'mse_fixmse_minssim': array([128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
@@ -235,120 +238,120 @@
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128., 128., 128., 128., 128., 128., 128., 128., 128., 128., 128.,\n",
        "         128.]),\n",
-       "  'ssim_fixmse_minssim': array([0.76177023, 0.75174074, 0.74281362, 0.7345279 , 0.72659961,\n",
-       "         0.71888015, 0.71133438, 0.70402473, 0.69708094, 0.69065122,\n",
-       "         0.68485275, 0.67973971, 0.6752977 , 0.67146344, 0.66815475,\n",
-       "         0.6652925 , 0.66280891, 0.66064725, 0.65875972, 0.65710576,\n",
-       "         0.65565102, 0.65436667, 0.65322864, 0.65221692, 0.65131489,\n",
-       "         0.65050865, 0.64978656, 0.64913878, 0.64855688, 0.64803365,\n",
-       "         0.64756278, 0.64713876, 0.64675673, 0.64641237, 0.64610182,\n",
-       "         0.64582164, 0.64556875, 0.64534039, 0.64513407, 0.64494759,\n",
-       "         0.64477896, 0.64462639, 0.64448828, 0.64436322, 0.64424991,\n",
-       "         0.64414722, 0.6440541 , 0.64396964, 0.643893  , 0.64382344,\n",
-       "         0.64376028, 0.64370292, 0.64365082, 0.64360347, 0.64356044,\n",
-       "         0.64352133, 0.64348577, 0.64345343, 0.64342402, 0.64339727,\n",
-       "         0.64337293, 0.64335079, 0.64333065, 0.64331233, 0.64329566,\n",
-       "         0.64328049, 0.64326669, 0.64325414, 0.64324272, 0.64323234,\n",
-       "         0.6432229 , 0.64321432, 0.64320652, 0.64319943, 0.643193  ,\n",
-       "         0.64318716, 0.64318185, 0.64317705, 0.64317269, 0.64316875,\n",
-       "         0.64316518, 0.64316195, 0.64315903, 0.6431564 , 0.64315403,\n",
-       "         0.64315189, 0.64314998, 0.64314826, 0.64314671, 0.64314534,\n",
-       "         0.64314411, 0.64314302, 0.64314205, 0.64314119, 0.64314044,\n",
-       "         0.64313978, 0.6431392 , 0.64313871, 0.64313828, 0.64313791]),\n",
-       "  'maxssim': 0.9944229185693149,\n",
-       "  'minssim': 0.6431379137832185,\n",
-       "  'mse_fixssim_minmse': array([127.44616484, 126.93449484, 126.43093603, 125.93530494,\n",
-       "         125.44742256, 124.96711445, 124.49421062, 124.02854549,\n",
-       "         123.56995773, 123.11829015, 122.67338962, 122.23510691,\n",
-       "         121.80329666, 121.37781721, 120.95853053, 120.54530213,\n",
-       "         120.13800095, 119.73649929, 119.3406727 , 118.95039989,\n",
-       "         118.56556269, 118.18604589, 117.81173723, 117.4425273 ,\n",
-       "         117.07830944, 116.7189797 , 116.36443675, 116.01458182,\n",
-       "         115.66931862, 115.32855326, 114.99219424, 114.66015232,\n",
-       "         114.33234051, 114.00867398, 113.68907001, 113.37344795,\n",
-       "         113.06172914, 112.75383686, 112.44969631, 112.1492345 ,\n",
-       "         111.85238027, 111.55906419, 111.26921853, 110.98277723,\n",
-       "         110.69967582, 110.41985143, 110.14324268, 109.86978971,\n",
-       "         109.59943409, 109.3321188 , 109.0677882 , 108.80638799,\n",
-       "         108.54786516, 108.29216796, 108.03924588, 107.78904963,\n",
-       "         107.54153106, 107.29664317, 107.05434008, 106.81457696,\n",
-       "         106.57731006, 106.34249664, 106.11009496, 105.88006426,\n",
-       "         105.6523647 , 105.42695739, 105.20380433, 104.98286838,\n",
-       "         104.76411326, 104.54750353, 104.33300454, 104.12058244,\n",
-       "         103.91020414, 103.70183731, 103.49545032, 103.29101228,\n",
-       "         103.08849297, 102.88786286, 102.68909306, 102.49215533,\n",
-       "         102.29702206, 102.10366622, 101.91206141, 101.72218178,\n",
-       "         101.53400204, 101.34749746, 101.16264385, 100.97941753,\n",
-       "         100.79779533, 100.61775457, 100.43927305, 100.26232907,\n",
-       "         100.08690134,  99.91296906,  99.74051185,  99.56950974,\n",
-       "          99.39994319,  99.23179307,  99.06504063,  98.89966751]),\n",
-       "  'ssim_fixssim_minmse': array([0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278,\n",
-       "         0.77358278, 0.77358278, 0.77358278, 0.77358278, 0.77358278]),\n",
-       "  'mse_fixssim_maxmse': array([ 133.36840117,  139.73263466,  147.22175529,  156.0532816 ,\n",
-       "          166.5477139 ,  179.16700042,  194.01703577,  211.59734443,\n",
-       "          222.02963577,  233.22301044,  245.49919496,  258.84818457,\n",
-       "          273.28476987,  281.27944929,  289.37643047,  297.86125826,\n",
-       "          306.66423243,  315.77540779,  325.22393292,  335.01074814,\n",
-       "          345.13131378,  355.54326801,  366.32613042,  377.50772143,\n",
-       "          389.05491287,  401.02519201,  413.3662292 ,  426.08687181,\n",
-       "          439.20348458,  452.7286325 ,  466.6625399 ,  480.81891963,\n",
-       "          494.84566849,  509.25930839,  524.12191354,  539.41296938,\n",
-       "          555.18146183,  571.42478708,  588.11678544,  605.3031113 ,\n",
-       "          623.05484382,  641.05207887,  659.50018495,  678.53215955,\n",
-       "          698.22118414,  718.48989604,  739.34852136,  760.86799808,\n",
-       "          783.217974  ,  806.14137656,  830.01894603,  854.79217185,\n",
-       "          880.51013716,  907.2634161 ,  934.76597602,  949.32601575,\n",
-       "          963.64354121,  978.04663559,  992.79116712, 1007.80307773,\n",
-       "         1023.12528618, 1038.72773177, 1054.68558136, 1070.86384953,\n",
-       "         1087.44896245, 1104.16862446, 1121.34497292, 1138.92025887,\n",
-       "         1156.87843045, 1175.25935827, 1194.03920573, 1213.23325103,\n",
-       "         1232.87581071, 1252.95779925, 1273.36744538, 1294.32164924,\n",
-       "         1315.74240127, 1337.60425841, 1359.78124008, 1382.43583488,\n",
-       "         1405.58690221, 1416.59652056, 1428.27162639, 1440.05942681,\n",
-       "         1451.99800406, 1464.04617657, 1476.1875303 , 1488.44040392,\n",
-       "         1500.72913376, 1513.05167229, 1525.47326013, 1537.94550723,\n",
-       "         1550.51142151, 1563.15111224, 1575.76685302, 1581.30640425,\n",
-       "         1587.49531914, 1593.80004188, 1600.11640698, 1606.44535689]),\n",
-       "  'ssim_fixssim_maxmse': array([0.77360243, 0.77358968, 0.77359766, 0.77363698, 0.7736888 ,\n",
-       "         0.773727  , 0.77379725, 0.773898  , 0.77378393, 0.77382653,\n",
-       "         0.77384454, 0.77387588, 0.77390504, 0.77374714, 0.7737538 ,\n",
-       "         0.77374931, 0.77375292, 0.77375798, 0.7737619 , 0.77376753,\n",
-       "         0.77376893, 0.77378622, 0.7737914 , 0.77379057, 0.77379401,\n",
-       "         0.77379086, 0.77379598, 0.77380018, 0.77380172, 0.77379832,\n",
-       "         0.77379471, 0.77379756, 0.77384212, 0.77385021, 0.77384536,\n",
-       "         0.77383408, 0.77382531, 0.77382238, 0.77381277, 0.77380568,\n",
-       "         0.7737949 , 0.77382691, 0.77383388, 0.77383155, 0.77383203,\n",
-       "         0.77384508, 0.77384595, 0.77384982, 0.77384352, 0.77386479,\n",
-       "         0.77386214, 0.77386667, 0.7738763 , 0.77387259, 0.77388375,\n",
-       "         0.77375405, 0.77374643, 0.77376391, 0.77376573, 0.77376711,\n",
-       "         0.77376323, 0.77376017, 0.77374811, 0.77375467, 0.77374082,\n",
-       "         0.773749  , 0.77373224, 0.77370932, 0.77368712, 0.77365904,\n",
-       "         0.77363196, 0.77360681, 0.77357806, 0.77355111, 0.77352533,\n",
-       "         0.77348049, 0.77343453, 0.77339346, 0.77335441, 0.77331693,\n",
-       "         0.77327246, 0.77337758, 0.77338328, 0.77338866, 0.77338461,\n",
-       "         0.77337887, 0.77337017, 0.77335676, 0.77335169, 0.77334979,\n",
-       "         0.77333996, 0.77333099, 0.77331435, 0.77329225, 0.77328026,\n",
-       "         0.77339417, 0.77340812, 0.77340467, 0.77340076, 0.77339659]),\n",
-       "  'minmse': 98.89966750759348,\n",
-       "  'maxmse': 1606.4453568897088,\n",
+       "  'ssim_fixmse_minssim': array([0.81069721, 0.80415136, 0.79827382, 0.7927989 , 0.78758476,\n",
+       "         0.7825861 , 0.77782948, 0.77338629, 0.76933555, 0.7657286 ,\n",
+       "         0.76258054, 0.75987991, 0.75759382, 0.75567185, 0.75405628,\n",
+       "         0.7526936 , 0.75154034, 0.75056265, 0.74973342, 0.74902986,\n",
+       "         0.74843227, 0.74792359, 0.7474892 , 0.74711674, 0.74679588,\n",
+       "         0.74651809, 0.74627635, 0.74606493, 0.74587912, 0.74571508,\n",
+       "         0.74556965, 0.74544023, 0.74532467, 0.74522116, 0.74512817,\n",
+       "         0.74504441, 0.74496878, 0.74490032, 0.74483822, 0.74478177,\n",
+       "         0.74473035, 0.74468342, 0.74464051, 0.74460123, 0.7445652 ,\n",
+       "         0.74453212, 0.74450172, 0.74447375, 0.74444799, 0.74442426,\n",
+       "         0.74440239, 0.74438221, 0.7443636 , 0.74434643, 0.74433057,\n",
+       "         0.74431594, 0.74430244, 0.74428997, 0.74427846, 0.74426784,\n",
+       "         0.74425803, 0.74424898, 0.74424062, 0.74423291, 0.74422579,\n",
+       "         0.74421923, 0.74421317, 0.74420758, 0.74420242, 0.74419767,\n",
+       "         0.74419328, 0.74418924, 0.74418551, 0.74418208, 0.74417891,\n",
+       "         0.74417599, 0.74417331, 0.74417083, 0.74416855, 0.74416646,\n",
+       "         0.74416453, 0.74416275, 0.74416112, 0.74415963, 0.74415825,\n",
+       "         0.74415699, 0.74415583, 0.74415477, 0.7441538 , 0.74415291,\n",
+       "         0.7441521 , 0.74415135, 0.74415068, 0.74415006, 0.7441495 ,\n",
+       "         0.74414899, 0.74414853, 0.74414811, 0.74414773, 0.74414739]),\n",
+       "  'maxssim': 0.99290440833461,\n",
+       "  'minssim': 0.7441473913547447,\n",
+       "  'mse_fixssim_minmse': array([127.62569966, 127.25907758, 126.89830629, 126.5432421 ,\n",
+       "         126.19375253, 125.84970824, 125.51098323, 125.17745477,\n",
+       "         124.84900331, 124.5255124 , 124.2068686 , 123.89296145,\n",
+       "         123.58368333, 123.27892946, 122.97859775, 122.68258882,\n",
+       "         122.39080584, 122.10315454, 121.81954311, 121.53988213,\n",
+       "         121.26408454, 120.99206555, 120.72374258, 120.45903523,\n",
+       "         120.19786522, 119.94015629, 119.68583422, 119.43482671,\n",
+       "         119.18706338, 118.94247568, 118.70099686, 118.46256195,\n",
+       "         118.22710767, 117.99457239, 117.76489611, 117.53802042,\n",
+       "         117.31388843, 117.09244474, 116.87363542, 116.65740794,\n",
+       "         116.44371116, 116.23249528, 116.02371179, 115.81731348,\n",
+       "         115.61325436, 115.41148964, 115.21197571, 115.01467011,\n",
+       "         114.81953145, 114.62651948, 114.43559495, 114.24671966,\n",
+       "         114.0598564 , 113.87496892, 113.69202191, 113.510981  ,\n",
+       "         113.33181268, 113.15448433, 112.97896417, 112.80522123,\n",
+       "         112.63322535, 112.46294714, 112.29435797, 112.12742994,\n",
+       "         111.96213588, 111.79844929, 111.63634436, 111.47579594,\n",
+       "         111.31677951, 111.15927119, 111.00324769, 110.8486863 ,\n",
+       "         110.6955649 , 110.54386192, 110.39355633, 110.24462762,\n",
+       "         110.09705579, 109.95082136, 109.8059053 , 109.66228908,\n",
+       "         109.5199546 , 109.37888422, 109.23906072, 109.10046731,\n",
+       "         108.96308761, 108.82690563, 108.69190575, 108.55807275,\n",
+       "         108.42539176, 108.29384826, 108.16342808, 108.03411739,\n",
+       "         107.90590266, 107.77877069, 107.6527086 , 107.52770378,\n",
+       "         107.40374392, 107.280817  , 107.15891125, 107.03801519]),\n",
+       "  'ssim_fixssim_minmse': array([0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846,\n",
+       "         0.81831846, 0.81831846, 0.81831846, 0.81831846, 0.81831846]),\n",
+       "  'mse_fixssim_maxmse': array([ 131.81989257,  136.34348117,  141.63201819,  147.93891278,\n",
+       "          155.38338115,  164.2072263 ,  174.58969259,  180.77038681,\n",
+       "          187.35687873,  194.48181227,  198.57370304,  202.66885863,\n",
+       "          206.95343129,  211.44231618,  216.13691245,  221.02634466,\n",
+       "          226.11581241,  231.45406809,  237.02262995,  242.83028251,\n",
+       "          248.81360619,  255.03345526,  261.46183743,  268.20589445,\n",
+       "          271.92855589,  275.50581231,  279.16591822,  282.90724991,\n",
+       "          286.73714745,  290.64830841,  294.64536986,  298.72977854,\n",
+       "          302.9070491 ,  307.17661949,  311.52367919,  315.9615864 ,\n",
+       "          320.49015071,  325.12141654,  329.86378322,  334.71438487,\n",
+       "          339.66399361,  344.73644098,  349.93210914,  355.25219363,\n",
+       "          360.69572122,  366.26837608,  371.96846258,  377.80329199,\n",
+       "          383.77938626,  389.88638347,  396.14353339,  402.54835547,\n",
+       "          409.12344679,  415.86473831,  422.7769448 ,  429.86538679,\n",
+       "          437.11955142,  444.55628708,  452.18579893,  460.02165701,\n",
+       "          468.0649593 ,  476.29650757,  484.7347087 ,  493.40456576,\n",
+       "          502.30552879,  511.44021594,  520.83114704,  530.48547572,\n",
+       "          540.40541267,  550.59925317,  561.07367344,  571.86215993,\n",
+       "          582.96345493,  594.39243813,  606.16232589,  618.28830266,\n",
+       "          630.7534013 ,  643.57218989,  656.77008653,  670.38608448,\n",
+       "          684.42229927,  698.90648368,  713.75502045,  729.09525608,\n",
+       "          744.89191175,  761.03686543,  777.65684378,  794.83179371,\n",
+       "          812.49479186,  830.66898008,  849.46972268,  868.84766347,\n",
+       "          888.75751215,  909.25712128,  930.36668338,  952.1656941 ,\n",
+       "          974.65873922,  997.87604088, 1021.77859313, 1046.42121422]),\n",
+       "  'ssim_fixssim_maxmse': array([0.81838315, 0.81837394, 0.81840433, 0.81841667, 0.8184811 ,\n",
+       "         0.81854265, 0.81865555, 0.8185667 , 0.81860168, 0.8186602 ,\n",
+       "         0.81849321, 0.81849264, 0.81849454, 0.81849667, 0.81850134,\n",
+       "         0.81850698, 0.81852163, 0.8185241 , 0.81853312, 0.81854646,\n",
+       "         0.81856169, 0.81858209, 0.81861676, 0.8186252 , 0.8184758 ,\n",
+       "         0.81848028, 0.81848157, 0.81848229, 0.8184804 , 0.81848067,\n",
+       "         0.81848019, 0.81847912, 0.81847739, 0.81847648, 0.81848131,\n",
+       "         0.81848611, 0.81848742, 0.81848633, 0.81848348, 0.81848151,\n",
+       "         0.81848324, 0.81848096, 0.81847737, 0.81847369, 0.81847083,\n",
+       "         0.81846829, 0.81846601, 0.81846364, 0.81846065, 0.81845897,\n",
+       "         0.81845727, 0.8184584 , 0.81845481, 0.81845096, 0.81844714,\n",
+       "         0.81844336, 0.81844286, 0.81844313, 0.8184418 , 0.81843787,\n",
+       "         0.81843385, 0.81843244, 0.81843006, 0.81842643, 0.81842301,\n",
+       "         0.81842012, 0.8184164 , 0.81841261, 0.81841113, 0.81841123,\n",
+       "         0.81841167, 0.81840845, 0.81840571, 0.81840289, 0.81840006,\n",
+       "         0.81839648, 0.81839794, 0.81840175, 0.81840656, 0.81840632,\n",
+       "         0.8184053 , 0.8184015 , 0.81841222, 0.8184118 , 0.81841594,\n",
+       "         0.81844041, 0.81845594, 0.81846008, 0.81847317, 0.81848535,\n",
+       "         0.81848382, 0.81848619, 0.81849768, 0.81851117, 0.81852665,\n",
+       "         0.81853106, 0.81853637, 0.81854015, 0.81854653, 0.81855083]),\n",
+       "  'minmse': 107.03801518643529,\n",
+       "  'maxmse': 1046.4212142210524,\n",
        "  'noise_level': 128,\n",
        "  'original_image': 'samp6'}}"
       ]
diff --git a/plenoptic/metric/__init__.py b/plenoptic/metric/__init__.py
index 15ea58dc..322d7c17 100644
--- a/plenoptic/metric/__init__.py
+++ b/plenoptic/metric/__init__.py
@@ -1,4 +1,4 @@
-from .perceptual_distance import ssim, nlpd, nspd, ssim_map
+from .perceptual_distance import ssim, ms_ssim, nlpd, nspd, ssim_map
 from .model_metric import model_metric
 from .naive import mse
 from .classes import NLP
diff --git a/plenoptic/metric/perceptual_distance.py b/plenoptic/metric/perceptual_distance.py
index 372e044c..55a6e204 100644
--- a/plenoptic/metric/perceptual_distance.py
+++ b/plenoptic/metric/perceptual_distance.py
@@ -90,21 +90,16 @@ def _ssim_parts(img1, img2, dynamic_range):
     C1 = (0.01 * dynamic_range) ** 2
     C2 = (0.03 * dynamic_range) ** 2
 
-    v1 = 2.0 * sigma12 + C2
-    v2 = sigma1_sq + sigma2_sq + C2
-
-    # SSIM consists of a luminance component, a contrast component, and a
-    # structure component. This is the contrast component, which is used to
-    # compute MS-SSIM This is the contrast component, which is used to compute
-    # MS-SSIM.
-    contrast_map = v1 / v2
+    # SSIM is the product of a luminance component, a contrast component, and a
+    # structure component. The contrast-structure component has to be separated
+    # when computing MS-SSIM.
+    luminance_map = (2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)
+    contrast_structure_map = (2.0 * sigma12 + C2) / (sigma1_sq + sigma2_sq + C2)
+    map_ssim = luminance_map * contrast_structure_map
 
     # the weight used for stability
-    weight = torch.log(torch.matmul((1+(sigma1_sq/C2)), (1+(sigma2_sq/C2))))
-
-    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
-    ssim_map = ((2 * mu1_mu2 + C1) / (mu1_sq + mu2_sq + C1)) * contrast_map
-    return ssim_map, contrast_map, weight
+    weight = torch.log((1 + sigma1_sq/C2) * (1 + sigma2_sq/C2))
+    return map_ssim, contrast_structure_map, weight
 
 
 def ssim(img1, img2, weighted=False, dynamic_range=1):
@@ -113,7 +108,7 @@ def ssim(img1, img2, weighted=False, dynamic_range=1):
     As described in [1]_, the structural similarity index (SSIM) is a
     perceptual distance metric, giving the distance between two images. SSIM is
     based on three comparison measurements between the two images: luminance,
-    contrast, and structure. All of these are computed in windows across the
+    contrast, and structure. All of these are computed convolutionally across the
     images. See the references for more information.
 
     This implementation follows the original implementation, as found at [2]_,
@@ -151,8 +146,8 @@ def ssim(img1, img2, weighted=False, dynamic_range=1):
     Returns
     ------
     mssim : torch.Tensor
-        2d tensor containing the mean SSIM for each image, averaged over the
-        whole image
+        2d tensor of shape (batch, channel) containing the mean SSIM for each
+        image, averaged over the whole image
 
     Notes
     -----
@@ -169,7 +164,7 @@ def ssim(img1, img2, weighted=False, dynamic_range=1):
     ----------
     .. [1] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
        quality assessment: From error measurement to structural similarity"
-       IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
+       IEEE Transactions on Image Processing, vol. 13, no. 1, Jan. 2004.
     .. [2] [matlab code](https://www.cns.nyu.edu/~lcv/ssim/ssim_index.m)
     .. [3] [project page](https://www.cns.nyu.edu/~lcv/ssim/)
     .. [4] Wang, Z., & Simoncelli, E. P. (2008). Maximum differentiation (MAD)
@@ -186,6 +181,10 @@ def ssim(img1, img2, weighted=False, dynamic_range=1):
     else:
         mssim = (map_ssim*weight).sum((-1, -2)) / weight.sum((-1, -2))
 
+    if min(img1.shape[2], img1.shape[3]) < 11:
+        warnings.warn("SSIM uses 11x11 convolutional kernel, but the height and/or "
+                      "the width of the input image is smaller than 11, so the "
+                      "kernel size is set to be the minimum of these two numbers.")
     return mssim
 
 
@@ -195,7 +194,7 @@ def ssim_map(img1, img2, dynamic_range=1):
     As described in [1]_, the structural similarity index (SSIM) is a
     perceptual distance metric, giving the distance between two images. SSIM is
     based on three comparison measurements between the two images: luminance,
-    contrast, and structure. All of these are computed in windows across the
+    contrast, and structure. All of these are computed convolutionally across the
     images. See the references for more information.
 
     This implementation follows the original implementation, as found at [2]_,
@@ -238,7 +237,7 @@ def ssim_map(img1, img2, dynamic_range=1):
     ----------
     .. [1] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
        quality assessment: From error measurement to structural similarity"
-       IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
+       IEEE Transactions on Image Processing, vol. 13, no. 1, Jan. 2004.
     .. [2] [matlab code](https://www.cns.nyu.edu/~lcv/ssim/ssim_index.m)
     .. [3] [project page](https://www.cns.nyu.edu/~lcv/ssim/)
     .. [4] Wang, Z., & Simoncelli, E. P. (2008). Maximum differentiation (MAD)
@@ -247,9 +246,97 @@ def ssim_map(img1, img2, dynamic_range=1):
        http://dx.doi.org/10.1167/8.12.8
 
     """
+    if min(img1.shape[2], img1.shape[3]) < 11:
+        warnings.warn("SSIM uses 11x11 convolutional kernel, but the height and/or "
+                      "the width of the input image is smaller than 11, so the "
+                      "kernel size is set to be the minimum of these two numbers.")
     return _ssim_parts(img1, img2, dynamic_range)[0]
 
 
+def ms_ssim(img1, img2, dynamic_range=1, power_factors=None):
+    r"""Multiscale structural similarity index (MS-SSIM)
+
+    As described in [1]_, multiscale structural similarity index (MS-SSIM) is
+    an improvement upon structural similarity index (SSIM) that takes into
+    account the perceptual distance between two images on different scales.
+
+    SSIM is based on three comparison measurements between the two images:
+    luminance, contrast, and structure. All of these are computed convolutionally
+    across the images, producing three maps instead of scalars. The SSIM map is
+    the elementwise product of these three maps. See `metric.ssim` and
+    `metric.ssim_map` for a full description of SSIM.
+
+    To get images of different scales, average pooling operations with kernel
+    size 2 are performed recursively on the input images. The product of
+    contrast map and structure map (the "contrast-structure map") is computed
+    for all but the coarsest scales, and the overall SSIM map is only computed
+    for the coarsest scale. Their mean values are raised to exponents and
+    multiplied to produce MS-SSIM:
+    .. math::
+        MSSSIM = {SSIM}_M^{a_M} \prod_{i=1}^{M-1} ({CS}_i)^{a_i}
+    Here :math: `M` is the number of scales, :math: `{CS}_i` is the mean value
+    of the contrast-structure map for the i'th finest scale, and :math: `{SSIM}_M`
+    is the mean value of the SSIM map for the coarsest scale. If at least one
+    of these terms are negative, the value of MS-SSIM is zero. The values of
+    :math: `a_i, i=1,...,M` are taken from the argument `power_factors`.
+
+    Parameters
+    ----------
+    img1 : torch.Tensor
+        4d tensor with first image to compare
+    img2 : torch.Tensor
+        4d tensor with second image to compare. Must have the same height and
+        width (last two dimensions) as `img1`
+    dynamic_range : int, optional.
+        dynamic range of the images. Note we assume that both images have the
+        same dynamic range. 1, the default, is appropriate for float images
+        between 0 and 1, as is common in synthesis. 2 is appropriate for float
+        images between -1 and 1, and 255 is appropriate for standard 8-bit
+        integer images. We'll raise a warning if it looks like your value is
+        not appropriate for `img1` or `img2`, but will calculate it anyway.
+    power_factors : 1D array, optional.
+        power exponents for the mean values of maps, for different scales (from
+        fine to coarse). The length of this array determines the number of scales.
+        By default, this is set to [0.0448, 0.2856, 0.3001, 0.2363, 0.1333],
+        which is what psychophysical experiments in [1]_ found.
+
+    Returns
+    ------
+    msssim : torch.Tensor
+        2d tensor of shape (batch, channel) containing the MS-SSIM for each image
+
+    References
+    ----------
+    .. [1] Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik. "Multiscale
+       structural similarity for image quality assessment." The Thrity-Seventh
+       Asilomar Conference on Signals, Systems & Computers, 2003. Vol. 2. IEEE, 2003.
+
+    """
+    if power_factors is None:
+        power_factors = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]
+
+    def downsample(img):
+        img = F.pad(img, (0, img.shape[3] % 2, 0, img.shape[2] % 2), mode="replicate")
+        img = F.avg_pool2d(img, kernel_size=2)
+        return img
+
+    msssim = 1
+    for i in range(len(power_factors) - 1):
+        _, contrast_structure_map, _ = _ssim_parts(img1, img2, dynamic_range)
+        msssim *= F.relu(contrast_structure_map.mean((-1, -2))).pow(power_factors[i])
+        img1 = downsample(img1)
+        img2 = downsample(img2)
+    map_ssim, _, _ = _ssim_parts(img1, img2, dynamic_range)
+    msssim *= F.relu(map_ssim.mean((-1, -2))).pow(power_factors[-1])
+
+    if min(img1.shape[2], img1.shape[3]) < 11:
+        warnings.warn("SSIM uses 11x11 convolutional kernel, but for some scales "
+                      "of the input image, the height and/or the width is smaller "
+                      "than 11, so the kernel size in SSIM is set to be the "
+                      "minimum of these two numbers for these scales.")
+    return msssim
+
+
 def normalized_laplacian_pyramid(im):
     """computes the normalized Laplacian Pyramid using pre-optimized parameters
 
@@ -273,7 +360,8 @@ def normalized_laplacian_pyramid(im):
     padd = 2
     normalized_laplacian_activations = []
     for N_b in range(0, N_scales):
-        filt = torch.tensor(spatialpooling_filters[N_b], dtype=torch.float32, device=im.device).unsqueeze(0).unsqueeze(0)
+        filt = torch.tensor(spatialpooling_filters[N_b], dtype=torch.float32,
+                            device=im.device).unsqueeze(0).unsqueeze(0)
         filtered_activations = F.conv2d(torch.abs(laplacian_activations[N_b]), filt, padding=padd, groups=channel)
         normalized_laplacian_activations.append(laplacian_activations[N_b] / (sigmas[N_b] + filtered_activations))
 
@@ -314,7 +402,8 @@ def nlpd(IM_1, IM_2):
 
     References
     ----------
-    .. [1] Laparra, V., Ballé, J., Berardino, A. and Simoncelli, E.P., 2016. Perceptual image quality assessment using a normalized Laplacian pyramid. Electronic Imaging, 2016(16), pp.1-6.
+    .. [1] Laparra, V., Ballé, J., Berardino, A. and Simoncelli, E.P., 2016. Perceptual image quality
+       assessment using a normalized Laplacian pyramid. Electronic Imaging, 2016(16), pp.1-6.
     """
 
     y = normalized_laplacian_pyramid(torch.cat((IM_1, IM_2), 0))
diff --git a/tests/test_plenoptic.py b/tests/test_plenoptic.py
index 3d8a4288..8e86e46b 100644
--- a/tests/test_plenoptic.py
+++ b/tests/test_plenoptic.py
@@ -14,7 +14,7 @@
 
 # If you add anything here, remember to update the docstring in osf_download!
 OSF_URL = {'plenoptic-test-files.tar.gz': 'q9kn8', 'ssim_images.tar.gz': 'j65tw',
-           'ssim_analysis.mat': 'ndtc7', 'MAD_results.tar.gz': 'jwcsr'}
+           'ssim_analysis.mat': 'ndtc7', 'msssim_images.tar.gz': '5fuba', 'MAD_results.tar.gz': 'jwcsr'}
 
 
 def osf_download(filename):
@@ -28,7 +28,7 @@ def osf_download(filename):
     Parameters
     ----------
     filename : {'plenoptic-test-files.tar.gz', 'ssim_images.tar.gz',
-                'ssim_analysis.mat', 'MAD_results.tar.gz'}
+                'ssim_analysis.mat', 'msssim_images.tar.gz', 'MAD_results.tar.gz'}
         Which file to download.
 
     Returns
@@ -74,6 +74,11 @@ def ssim_images():
     return osf_download('ssim_images.tar.gz')
 
 
+@pytest.fixture()
+def msssim_images():
+    return osf_download('msssim_images.tar.gz')
+
+
 @pytest.fixture()
 def ssim_analysis():
     ssim_analysis = osf_download('ssim_analysis.mat')
@@ -168,7 +173,11 @@ def test_ssim(self, einstein_img, curie_img, weighted):
         curie_img.requires_grad_()
         assert po.metric.ssim(einstein_img, curie_img, weighted=weighted).requires_grad
 
-    @pytest.mark.parametrize('func_name', ['noise', 'mse', 'ssim'])
+    def test_msssim(self, einstein_img, curie_img):
+        curie_img.requires_grad_()
+        assert po.metric.ms_ssim(einstein_img, curie_img).requires_grad
+
+    @pytest.mark.parametrize('func_name', ['noise', 'mse', 'ssim', 'ms-ssim'])
     @pytest.mark.parametrize('size_A', [1, 3])
     @pytest.mark.parametrize('size_B', [1, 2, 3])
     def test_batch_handling(self, einstein_img, curie_img, func_name, size_A, size_B):
@@ -176,12 +185,13 @@ def test_batch_handling(self, einstein_img, curie_img, func_name, size_A, size_B
             func = po.add_noise
             A = einstein_img.repeat(size_A, 1, 1, 1)
             B = size_B * [4]
-        elif func_name == 'mse':
-            func = po.metric.mse
-            A = einstein_img.repeat(size_A, 1, 1, 1)
-            B = curie_img.repeat(size_B, 1, 1, 1)
-        elif func_name == 'ssim':
-            func = po.metric.ssim
+        else:
+            if func_name == 'mse':
+                func = po.metric.mse
+            elif func_name == 'ssim':
+                func = po.metric.ssim
+            elif func_name == 'ms-ssim':
+                func = po.metric.ms_ssim
             A = einstein_img.repeat(size_A, 1, 1, 1)
             B = curie_img.repeat(size_B, 1, 1, 1)
         if size_A != size_B and size_A != 1 and size_B != 1:
@@ -238,6 +248,16 @@ def test_ssim_analysis(self, weighted, other_img, ssim_images,
         print(plen_val-mat_val, plen_val, mat_val)
         assert torch.allclose(plen_val, mat_val.view_as(plen_val), atol=1e-5)
 
+    def test_msssim_analysis(self, msssim_images):
+        # True values are defined by https://ece.uwaterloo.ca/~z70wang/research/iwssim/msssim.zip
+        true_values = torch.tensor([1.0000000, 0.9112161, 0.7699084, 0.8785111, 0.9488805], device=DEVICE)
+        computed_values = torch.zeros_like(true_values)
+        base_img = po.load_images(op.join(msssim_images, "samp0.tiff")).to(DEVICE)
+        for i in range(len(true_values)):
+            other_img = po.load_images(op.join(msssim_images, f"samp{i}.tiff")).to(DEVICE)
+            computed_values[i] = po.metric.ms_ssim(base_img, other_img)
+        assert torch.allclose(true_values, computed_values)
+
     def test_nlpd(self, einstein_img, curie_img):
         curie_img.requires_grad_()
         assert po.metric.nlpd(einstein_img, curie_img).requires_grad